{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\swhitt\Desktop\PoP replication log file.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 1 May 2025, 07:28:16

{com}. do "C:\Users\swhitt\Desktop\PoP replication do file.do"
{txt}
{com}. *Replication Instructions for
. 
. *Toward a Posthumanist Understanding of Wartime Suffering: 
. *Public Concern for Animal Welfare in Ukraine
. 
. *Sam Whitt, Vera Mironova
. 
. *Below are instructions for replicating all manuscript and online appendix tables and figures in STATA using the dataset "PoP replication data.dta". Please contact Sam Whitt (swhitt@highpoint.edu) for questions regarding data replication. See also the dofile "PoP replication do file". 
. 
. *Note: You may need to install STATA packages for the cibar, catcibar, and iebaltab commands. Use findit with the command name to identify and download the appropriate packets to install. 
. 
. *Note: In addition, some graphs require additional formatting using filename.grec files with the graph play command. To format a graph, simply run the command to generate the graph in the do file in STATA, then open the "Graph Editor" in STATA and click on the GREEN "Play Recording" button, then select "Browse" to select the grec file from the folder "grec files for STATA graph formatting" among Replication files. The name of the grec file is indicated in the note below the graph command in the do file for the specific graph you wish to format. This should automatically format the graph, which you may then save to a location of your choosing.
. 
. *Manuscript Replication
. 
. *"Stata user generated commands to install for replication purposes"
. 
. *"cibar"
. 
. ssc install cibar, replace
{txt}checking {hilite:cibar} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. 
. *"iebaltab from ietoolkit"
. 
. ssc install ietoolkit, replace
{txt}checking {hilite:ietoolkit} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. 
. *"catcibar"
. 
. net install catcibar, from("https://aarondwolf.github.io/catcibar") replace
checking {hilite:catcibar} consistency and verifying not already installed...
all files already exist and are up to date.
{txt}
{com}. 
. *Manuscript Replication
. 
. *In Text Replication
. 
. *"Overall, we find only modest treatment effects on both priming on human and animal suffering on empathy-related items. Specifically, priming on human suffering led to a small increase in human empathy relative to control in Model 1 (Cohen's D = 0.13, two-sample Wilcoxon test z=2.47,  p=0.014) and respondents reacted negatively to priming on animal suffering in Model 4 which resulted in a modest increase in support for efforts to protect humans relative to animals (Cohen's D = 0.14, two-sample Wilcoxon test z=2.56,  p=0.011)." 
. 
. esize twosample revempmediatorcheck if humanimaltxt~=2, by(humanimaltxt)

{txt}Effect size based on mean comparison

                               Obs per group:
                                 Treatment 1 =        622
                               Control Group =        722
{res}{col 1}{text}{hline 20}{c TT}{hline 12}{hline 12}{hline 12}
{col 1}{text}        Effect size{col 21}{c |}   Estimate{col 34}    [95% conf. interval]
{res}{col 1}{text}{hline 20}{c +}{hline 12}{hline 12}{hline 12}
{col 1}{text}          Cohen's {it:d}{col 21}{c |}{result}{space 2} .1262879{col 34}{space 3}  .018936{col 46}{space 3} .2335928
{col 1}{text}         Hedges's {it:g}{col 21}{c |}{result}{space 2} .1262173{col 34}{space 3} .0189255{col 46}{space 3} .2334622
{col 1}{text}{hline 20}{c BT}{hline 12}{hline 12}{hline 12}
{res}{txt}
{com}. ranksum revempmediatorcheck if humanimaltxt~=2, by(humanimaltxt)

{txt}Two-sample Wilcoxon rank-sum (Mann–Whitney) test

humanimaltxt {c |}      Obs    Rank sum    Expected
{hline 13}{c +}{hline 33}
 Treatment 1 {c |}{res}{col 17}    622{col 26}  433806.5{col 38}    418295
{txt}Control Grou {c |}{res}{col 17}    722{col 26}  470033.5{col 38}    485545
{txt}{hline 13}{c +}{hline 33}
    Combined {c |}{res}{col 17}   1344{col 26}    903840{col 38}    903840

{txt}Unadjusted variance{col 22}{res}  50334832
{txt}Adjustment for ties{col 22}{res} -10729387
{txt}{col 22}{hline 10}
Adjusted variance{col 22}{res}  39605444

{txt}H0: revemp~k(hum~ltxt==Treatment 1) = revemp~k(hum~ltxt==Control Group)
{col 10}z = {res}{ralign 6:2.465}
{txt}{col 1}Prob > |z| = {res}{ralign 6:0.0137}
{txt}
{com}. 
. esize twosample protectmore if humanimaltxt~=1, by(humanimaltxt)

{txt}Effect size based on mean comparison

                               Obs per group:
                                 Treatment 2 =        637
                               Control Group =        713
{res}{col 1}{text}{hline 20}{c TT}{hline 12}{hline 12}{hline 12}
{col 1}{text}        Effect size{col 21}{c |}   Estimate{col 34}    [95% conf. interval]
{res}{col 1}{text}{hline 20}{c +}{hline 12}{hline 12}{hline 12}
{col 1}{text}          Cohen's {it:d}{col 21}{c |}{result}{space 2}  .136307{col 34}{space 3} .0293016{col 46}{space 3} .2432619
{col 1}{text}         Hedges's {it:g}{col 21}{c |}{result}{space 2} .1362311{col 34}{space 3} .0292853{col 46}{space 3} .2431266
{col 1}{text}{hline 20}{c BT}{hline 12}{hline 12}{hline 12}
{res}{txt}
{com}. ranksum protectmore if humanimaltxt~=1, by(humanimaltxt)

{txt}Two-sample Wilcoxon rank-sum (Mann–Whitney) test

humanimaltxt {c |}      Obs    Rank sum    Expected
{hline 13}{c +}{hline 33}
 Treatment 2 {c |}{res}{col 17}    637{col 26}    447789{col 38}  430293.5
{txt}Control Grou {c |}{res}{col 17}    713{col 26}    464136{col 38}  481631.5
{txt}{hline 13}{c +}{hline 33}
    Combined {c |}{res}{col 17}   1350{col 26}    911925{col 38}    911925

{txt}Unadjusted variance{col 22}{res}  51133211
{txt}Adjustment for ties{col 22}{res}-4393993.7
{txt}{col 22}{hline 10}
Adjusted variance{col 22}{res}  46739217

{txt}H0: protec~e(hum~ltxt==Treatment 2) = protec~e(hum~ltxt==Control Group)
{col 10}z = {res}{ralign 6:2.559}
{txt}{col 1}Prob > |z| = {res}{ralign 6:0.0105}
{txt}
{com}. 
. *"Again, however, the overall treatment effect size is small (Cohen's D= 0.13, two sample Wilcoxon text z=1.957, p=0.050) indicating that the movement was relatively modest in the direction hypothesized by H2."
. 
. esize twosample giveidp100rnd if humanimalaidtxt~=1, by(humanimalaidtxt)

{txt}Effect size based on mean comparison

                               Obs per group:
                                 Treatment 2 =        653
                               Control Group =        658
{res}{col 1}{text}{hline 20}{c TT}{hline 12}{hline 12}{hline 12}
{col 1}{text}        Effect size{col 21}{c |}   Estimate{col 34}    [95% conf. interval]
{res}{col 1}{text}{hline 20}{c +}{hline 12}{hline 12}{hline 12}
{col 1}{text}          Cohen's {it:d}{col 21}{c |}{result}{space 2}-.1304874{col 34}{space 3}-.2388408{col 46}{space 3}-.0220843
{col 1}{text}         Hedges's {it:g}{col 21}{c |}{result}{space 2}-.1304126{col 34}{space 3}-.2387039{col 46}{space 3}-.0220716
{col 1}{text}{hline 20}{c BT}{hline 12}{hline 12}{hline 12}
{res}{txt}
{com}. ranksum giveidp100rnd if humanimalaidtxt~=1, by(humanimalaidtxt)

{txt}Two-sample Wilcoxon rank-sum (Mann–Whitney) test

humanimala~t {c |}      Obs    Rank sum    Expected
{hline 13}{c +}{hline 33}
 Treatment 2 {c |}{res}{col 17}    653{col 26}    415450{col 38}    428368
{txt}Control Grou {c |}{res}{col 17}    658{col 26}    444566{col 38}    431648
{txt}{hline 13}{c +}{hline 33}
    Combined {c |}{res}{col 17}   1311{col 26}    860016{col 38}    860016

{txt}Unadjusted variance{col 22}{res}  46977691
{txt}Adjustment for ties{col 22}{res}-3409487.9
{txt}{col 22}{hline 10}
Adjusted variance{col 22}{res}  43568203

{txt}H0: giveid~d(hum~dtxt==Treatment 2) = giveid~d(hum~dtxt==Control Group)
{col 10}z = {res}{ralign 6:-1.957}
{txt}{col 1}Prob > |z| = {res}{ralign 6:0.0503}
{txt}
{com}. 
. *"While few people chose extreme options of complete amnesty or extreme punishments such as the death penalty, the distribution shows that respondents handed down harsher punishments for actions leading to human deaths than animal deaths (two-sample t-test = 6.1, p<0.0001). A non-parametric equality of medians test is also significant when comparing punishments for human relative to animal deaths (Pearson's Chi-squared=25.1, p<0.001). The effect size is larger than what we have observed in previous experiments and should be considered a moderate treatment effect (Cohen's D= 0.29)."
. 
. ttest punishment, by(humandeathtxt) unpaired unequal

{txt}Two-sample t test with unequal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
       0 {c |}{res}{col 12}    861{col 22} 3.337979{col 34} .0473859{col 46} 1.390436{col 58} 3.244973{col 70} 3.430985
       {txt}1 {c |}{res}{col 12}    885{col 22} 3.740113{col 34} .0457883{col 46} 1.362152{col 58} 3.650247{col 70} 3.829979
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}  1,746{col 22}  3.54181{col 34} .0332749{col 46} 1.390398{col 58} 3.476547{col 70} 3.607073
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.4021339{col 34} .0658938{col 58}-.5313732{col 70}-.2728946
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}0{txt}) - mean({res}1{txt})                                      t = {res} -6.1028
{txt}H0: diff = 0                     Satterthwaite's degrees of freedom = {res} 1739.98

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}1.0000
{txt}
{com}. median punishment, by(humandeathtxt)

{txt}Median test

   Greater {c |} V242 1 = human death,
  than the {c |}   0 = animal death
    median {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
        no {c |}{res}       543        453 {txt}{c |}{res}       996 
{txt}       yes {c |}{res}       318        432 {txt}{c |}{res}       750 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       861        885 {txt}{c |}{res}     1,746 

{txt}          Pearson chi2({res}1{txt}) = {res} 25.1354  {txt} Pr = {res}0.000

{txt}   Continuity corrected:
          Pearson chi2({res}1{txt}) = {res} 24.6529{txt}   Pr = {res}0.000
{txt}
{com}. esize twosample punishment, by(humandeathtxt)

{txt}Effect size based on mean comparison

                               Obs per group:
                            humandeathtxt==0 =        861
                            humandeathtxt==1 =        885
{res}{col 1}{text}{hline 20}{c TT}{hline 12}{hline 12}{hline 12}
{col 1}{text}        Effect size{col 21}{c |}   Estimate{col 34}    [95% conf. interval]
{res}{col 1}{text}{hline 20}{c +}{hline 12}{hline 12}{hline 12}
{col 1}{text}          Cohen's {it:d}{col 21}{c |}{result}{space 2}-.2922119{col 34}{space 3}-.3864906{col 46}{space 3}-.1978503
{col 1}{text}         Hedges's {it:g}{col 21}{c |}{result}{space 2}-.2920862{col 34}{space 3}-.3863243{col 46}{space 3}-.1977652
{col 1}{text}{hline 20}{c BT}{hline 12}{hline 12}{hline 12}
{res}{txt}
{com}. 
. *Tables and Figures
. 
. *Table 1
. 
. tab nationality

      {txt}D4. With what nationality do you {c |}
                    associate yourself {c |}      Freq.     Percent        Cum.
{hline 39}{c +}{hline 35}
                             Ukrainian {c |}{res}      1,870       93.13       93.13
{txt}                               Russian {c |}{res}         58        2.89       96.02
{txt}Ukrainian and Russian (only voluntary) {c |}{res}         12        0.60       96.61
{txt}                               Belarus {c |}{res}          3        0.15       96.76
{txt}                             Moldavian {c |}{res}          2        0.10       96.86
{txt}                             Bulgarian {c |}{res}          2        0.10       96.96
{txt}                              Romanian {c |}{res}          3        0.15       97.11
{txt}                                  Pole {c |}{res}          3        0.15       97.26
{txt}                                   Jew {c |}{res}          6        0.30       97.56
{txt}                                 Other {c |}{res}         32        1.59       99.15
{txt}                           HARD TO SAY {c |}{res}         17        0.85      100.00
{txt}{hline 39}{c +}{hline 35}
                                 Total {c |}{res}      2,008      100.00
{txt}
{com}. tab russpeaker

 {txt}russpeaker {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,759       87.60       87.60
{txt}          1 {c |}{res}        249       12.40      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,008      100.00
{txt}
{com}. tab female

     {txt}female {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        877       43.68       43.68
{txt}          1 {c |}{res}      1,131       56.32      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,008      100.00
{txt}
{com}. sum age education income

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}      2,008    52.64044    16.04743         18         92
{txt}{space 3}education {c |}{res}      2,007    6.662182    1.433637          1          8
{txt}{space 6}income {c |}{res}      1,982     2.85671    .8872038          1          5
{txt}
{com}. tab region

  {txt}4 Regions {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       West {c |}{res}        388       19.32       19.32
{txt}    Central {c |}{res}        856       42.63       61.95
{txt}      South {c |}{res}        498       24.80       86.75
{txt}       East {c |}{res}        266       13.25      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,008      100.00
{txt}
{com}. tab ownpets

   {txt}do you own pets or farm {c |}
                   animals {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                        No {c |}{res}        645       32.15       32.15
{txt}                      Pets {c |}{res}        987       49.20       81.36
{txt}              Farm Animals {c |}{res}         44        2.19       83.55
{txt}Both pets and farm animals {c |}{res}        330       16.45      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}      2,006      100.00
{txt}
{com}. tab employment

                         {txt}employment d6 {c |}      Freq.     Percent        Cum.
{hline 39}{c +}{hline 35}
                    Worker, farmworker {c |}{res}        234       11.73       11.73
{txt}    Servant (without higher education) {c |}{res}        148        7.42       19.15
{txt}  Professional (with higher education) {c |}{res}        434       21.75       40.90
{txt}       Self employed businesswomen/men {c |}{res}         91        4.56       45.46
{txt}                  Entrepreneur, farmer {c |}{res}        123        6.17       51.63
{txt}                      Military servant {c |}{res}         38        1.90       53.53
{txt}                           Householder {c |}{res}        141        7.07       60.60
{txt}Pension (because of age or disability) {c |}{res}        691       34.64       95.24
{txt}                               Student {c |}{res}         26        1.30       96.54
{txt}                            Unemployed {c |}{res}         69        3.46      100.00
{txt}{hline 39}{c +}{hline 35}
                                 Total {c |}{res}      1,995      100.00
{txt}
{com}. tab dsawviolencehuman

{txt}dsawviolenc {c |}
     ehuman {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,580       79.72       79.72
{txt}          1 {c |}{res}        402       20.28      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,982      100.00
{txt}
{com}. tab dsawviolenceanimals

{txt}dsawviolenc {c |}
   eanimals {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,809       91.04       91.04
{txt}          1 {c |}{res}        178        8.96      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,987      100.00
{txt}
{com}. tab dinjured

   {txt}dinjured {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,041       51.84       51.84
{txt}          1 {c |}{res}        967       48.16      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,008      100.00
{txt}
{com}. tab dlostanimals

{txt}dlostanimal {c |}
          s {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,712       85.26       85.26
{txt}          1 {c |}{res}        296       14.74      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,008      100.00
{txt}
{com}. tab ddisplaced

 {txt}ddisplaced {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,745       86.90       86.90
{txt}          1 {c |}{res}        263       13.10      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,008      100.00
{txt}
{com}. 
. *Figure 2
. 
. histogram revempmediatorcheck, discrete percent addlabels addlabopts(mlabformat(%2.1f))
{txt}(start={res}0{txt}, width={res}1{txt})
{res}{txt}
{com}. graph save g1
{res}{txt}file {bf:g1.gph} saved

{com}. 
. histogram protectmoral, discrete percent addlabels addlabopts(mlabformat(%2.1f))
{txt}(start={res}0{txt}, width={res}1{txt})
{res}{txt}
{com}. graph save g2
{res}{txt}file {bf:g2.gph} saved

{com}. 
. histogram feelmorepain, discrete percent addlabels addlabopts(mlabformat(%2.1f))
{txt}(start={res}0{txt}, width={res}1{txt})
{res}{txt}
{com}. graph save g3
{res}{txt}file {bf:g3.gph} saved

{com}. 
. histogram protectmore, discrete percent addlabels addlabopts(mlabformat(%2.1f))
{txt}(start={res}0{txt}, width={res}1{txt})
{res}{txt}
{com}. graph save g4
{res}{txt}file {bf:g4.gph} saved

{com}. 
. graph combine "g1.gph" "g2.gph" "g3.gph" "g4.gph"
{res}{txt}
{com}. 
. *Note additional formatting requires the "Figure 2 formatting.grec" file with the command graph play "Figure 2 formatting.grec" 
. 
. *Table 2. 
. 
. reg revempmediatorcheck ib3.humanimaltxt, robust

{txt}Linear regression                               Number of obs     = {res}     1,981
                                                {txt}F(2, 1978)        =  {res}     2.83
                                                {txt}Prob > F          = {res}    0.0592
                                                {txt}R-squared         = {res}    0.0029
                                                {txt}Root MSE          =    {res}  2.227

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revempmedi~k{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimaltxt {c |}
Treatment 1  {c |}{col 14}{res}{space 2} .2808695{col 26}{space 2} .1220683{col 37}{space 1}    2.30{col 46}{space 3}0.021{col 54}{space 4} .0414736{col 67}{space 3} .5202654
{txt}Treatment 2  {c |}{col 14}{res}{space 2} .0589415{col 26}{space 2}  .120005{col 37}{space 1}    0.49{col 46}{space 3}0.623{col 54}{space 4} -.176408{col 67}{space 3} .2942909
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 5.277008{col 26}{space 2} .0810668{col 37}{space 1}   65.09{col 46}{space 3}0.000{col 54}{space 4} 5.118023{col 67}{space 3} 5.435994
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg protectmoral ib3.humanimaltxt, robust

{txt}Linear regression                               Number of obs     = {res}     1,932
                                                {txt}F(2, 1929)        =  {res}     0.41
                                                {txt}Prob > F          = {res}    0.6646
                                                {txt}R-squared         = {res}    0.0004
                                                {txt}Root MSE          =    {res} 3.3946

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}protectmoral{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimaltxt {c |}
Treatment 1  {c |}{col 14}{res}{space 2} .0654156{col 26}{space 2} .1849632{col 37}{space 1}    0.35{col 46}{space 3}0.724{col 54}{space 4}-.2973333{col 67}{space 3} .4281644
{txt}Treatment 2  {c |}{col 14}{res}{space 2}-.1081186{col 26}{space 2} .1900418{col 37}{space 1}   -0.57{col 46}{space 3}0.569{col 54}{space 4}-.4808276{col 67}{space 3} .2645905
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 5.575107{col 26}{space 2} .1284157{col 37}{space 1}   43.41{col 46}{space 3}0.000{col 54}{space 4} 5.323259{col 67}{space 3} 5.826955
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg feelmorepain ib3.humanimaltxt, robust

{txt}Linear regression                               Number of obs     = {res}     1,938
                                                {txt}F(2, 1935)        =  {res}     0.48
                                                {txt}Prob > F          = {res}    0.6180
                                                {txt}R-squared         = {res}    0.0005
                                                {txt}Root MSE          =    {res} 3.5088

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}feelmorepain{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimaltxt {c |}
Treatment 1  {c |}{col 14}{res}{space 2} .0273301{col 26}{space 2} .1935847{col 37}{space 1}    0.14{col 46}{space 3}0.888{col 54}{space 4}-.3523265{col 67}{space 3} .4069868
{txt}Treatment 2  {c |}{col 14}{res}{space 2} .1787982{col 26}{space 2} .1923386{col 37}{space 1}    0.93{col 46}{space 3}0.353{col 54}{space 4}-.1984144{col 67}{space 3} .5560108
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 5.538244{col 26}{space 2}  .129899{col 37}{space 1}   42.63{col 46}{space 3}0.000{col 54}{space 4} 5.283487{col 67}{space 3}    5.793
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg protectmore ib3.humanimaltxt, robust

{txt}Linear regression                               Number of obs     = {res}     1,959
                                                {txt}F(2, 1956)        =  {res}     3.16
                                                {txt}Prob > F          = {res}    0.0427
                                                {txt}R-squared         = {res}    0.0032
                                                {txt}Root MSE          =    {res} 3.0967

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} protectmore{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimaltxt {c |}
Treatment 1  {c |}{col 14}{res}{space 2}  .169284{col 26}{space 2} .1722984{col 37}{space 1}    0.98{col 46}{space 3}0.326{col 54}{space 4}-.1686238{col 67}{space 3} .5071919
{txt}Treatment 2  {c |}{col 14}{res}{space 2} .4204073{col 26}{space 2} .1679175{col 37}{space 1}    2.50{col 46}{space 3}0.012{col 54}{space 4} .0910912{col 67}{space 3} .7497234
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 6.510519{col 26}{space 2} .1168801{col 37}{space 1}   55.70{col 46}{space 3}0.000{col 54}{space 4} 6.281296{col 67}{space 3} 6.739742
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Figure 3
. 
. histogram revaidmediatorcheck, discrete percent addlabels addlabopts(mlabformat(%2.1f))
{txt}(start={res}0{txt}, width={res}1{txt})
{res}{txt}
{com}. graph save g5
{res}{txt}file {bf:g5.gph} saved

{com}. 
. histogram providemoral, discrete percent addlabels addlabopts(mlabformat(%2.1f))
{txt}(start={res}0{txt}, width={res}1{txt})
{res}{txt}
{com}. graph save g6
{res}{txt}file {bf:g6.gph} saved

{com}. 
. histogram selfhelp, discrete percent addlabels addlabopts(mlabformat(%2.1f))
{txt}(start={res}0{txt}, width={res}1{txt})
{res}{txt}
{com}. graph save g7
{res}{txt}file {bf:g7.gph} saved

{com}. 
. histogram giveidp100rnd, discrete percent addlabels addlabopts(mlabformat(%2.1f)) barwidth(1) xlabel(0(1)10)
{txt}(start={res}0{txt}, width={res}1{txt})
{res}{txt}
{com}. graph save g8
{res}{txt}file {bf:g8.gph} saved

{com}. 
. graph combine "g5.gph" "g6.gph" "g7.gph" "g8.gph"
{res}{txt}
{com}. 
. *Note additional formatting requires the "Figure 3 formatting.grec" file with the command graph play "Figure 3 formatting.grec" 
. 
. *Table 3. 
. 
. reg revaidmediatorcheck ib3.humanimalaidtxt, robust

{txt}Linear regression                               Number of obs     = {res}     1,971
                                                {txt}F(2, 1968)        =  {res}     0.99
                                                {txt}Prob > F          = {res}    0.3726
                                                {txt}R-squared         = {res}    0.0010
                                                {txt}Root MSE          =    {res} 2.1329

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}revaidmediato~k{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimalaidtxt {c |}
{space 3}Treatment 1  {c |}{col 17}{res}{space 2}-.0433271{col 29}{space 2} .1192926{col 40}{space 1}   -0.36{col 49}{space 3}0.716{col 57}{space 4}-.2772801{col 70}{space 3}  .190626
{txt}{space 3}Treatment 2  {c |}{col 17}{res}{space 2}-.1588116{col 29}{space 2} .1178514{col 40}{space 1}   -1.35{col 49}{space 3}0.178{col 57}{space 4}-.3899382{col 70}{space 3}  .072315
{txt}{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2} 5.703988{col 29}{space 2} .0857062{col 40}{space 1}   66.55{col 49}{space 3}0.000{col 57}{space 4} 5.535903{col 70}{space 3} 5.872072
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg providemoral ib3.humanimalaidtxt, robust

{txt}Linear regression                               Number of obs     = {res}     1,945
                                                {txt}F(2, 1942)        =  {res}     0.35
                                                {txt}Prob > F          = {res}    0.7031
                                                {txt}R-squared         = {res}    0.0004
                                                {txt}Root MSE          =    {res}  3.121

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}   providemoral{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimalaidtxt {c |}
{space 3}Treatment 1  {c |}{col 17}{res}{space 2}-.1261303{col 29}{space 2} .1727352{col 40}{space 1}   -0.73{col 49}{space 3}0.465{col 57}{space 4}-.4648962{col 70}{space 3} .2126355
{txt}{space 3}Treatment 2  {c |}{col 17}{res}{space 2}-.1253858{col 29}{space 2} .1736059{col 40}{space 1}   -0.72{col 49}{space 3}0.470{col 57}{space 4}-.4658594{col 70}{space 3} .2150878
{txt}{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2} 5.624611{col 29}{space 2}  .122093{col 40}{space 1}   46.07{col 49}{space 3}0.000{col 57}{space 4} 5.385163{col 70}{space 3} 5.864058
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg selfhelp ib3.humanimalaidtxt, robust

{txt}Linear regression                               Number of obs     = {res}     1,965
                                                {txt}F(2, 1962)        =  {res}     0.11
                                                {txt}Prob > F          = {res}    0.9003
                                                {txt}R-squared         = {res}    0.0001
                                                {txt}Root MSE          =    {res} 2.6678

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}       selfhelp{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimalaidtxt {c |}
{space 3}Treatment 1  {c |}{col 17}{res}{space 2}  -.02375{col 29}{space 2}  .147604{col 40}{space 1}   -0.16{col 49}{space 3}0.872{col 57}{space 4}-.3132271{col 70}{space 3}  .265727
{txt}{space 3}Treatment 2  {c |}{col 17}{res}{space 2} .0431255{col 29}{space 2} .1462563{col 40}{space 1}    0.29{col 49}{space 3}0.768{col 57}{space 4}-.2437085{col 70}{space 3} .3299595
{txt}{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2} 8.116564{col 29}{space 2} .1029782{col 40}{space 1}   78.82{col 49}{space 3}0.000{col 57}{space 4} 7.914606{col 70}{space 3} 8.318523
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg giveidp100rnd ib3.humanimalaidtxt, robust

{txt}Linear regression                               Number of obs     = {res}     1,981
                                                {txt}F(2, 1978)        =  {res}     3.12
                                                {txt}Prob > F          = {res}    0.0445
                                                {txt}R-squared         = {res}    0.0032
                                                {txt}Root MSE          =    {res} 2.2547

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}  giveidp100rnd{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimalaidtxt {c |}
{space 3}Treatment 1  {c |}{col 17}{res}{space 2}-.0547203{col 29}{space 2} .1222233{col 40}{space 1}   -0.45{col 49}{space 3}0.654{col 57}{space 4}-.2944203{col 70}{space 3} .1849796
{txt}{space 3}Treatment 2  {c |}{col 17}{res}{space 2}-.2953914{col 29}{space 2} .1250623{col 40}{space 1}   -2.36{col 49}{space 3}0.018{col 57}{space 4} -.540659{col 70}{space 3}-.0501238
{txt}{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2} 6.221884{col 29}{space 2} .0864327{col 40}{space 1}   71.99{col 49}{space 3}0.000{col 57}{space 4} 6.052376{col 70}{space 3} 6.391393
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Figure 4
. 
. histogram punishment if humandeathtxt==0, discrete percent addlabels addlabopts(mlabformat(%2.1f))
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. graph save g9
{res}{txt}file {bf:g9.gph} saved

{com}. 
. histogram punishment if humandeathtxt==1, discrete percent addlabels addlabopts(mlabformat(%2.1f))
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. graph save g10
{res}{txt}file {bf:g10.gph} saved

{com}. 
. graph combine "g9.gph" "g10.gph" 
{res}{txt}
{com}. 
. *Note additional formatting requires the "Figure 4 formatting.grec" file with the command graph play "Figure 4 formatting.grec" 
. 
. *Figure 5
. 
. *Note: Use PoP comparison data.dta for this figure
. 
. *graph twoway (kdensity revanimal1 if ukraine==1) (kdensity revanimal1 if moldova==1) (kdensity revanimal1 if usa==1)
. *graph save g11
. 
. *histogram revanimal3, by(group, cols(3)) discrete percent addlabels addlabopts(mlabformat(%2.1f))
. *graph save g12
. 
. *graph combine "g11.gph" "g12.gph" 
. 
. *Note additional formatting requires the "Figure 5 formatting.grec" file with the command graph play "Figure 5 formatting.grec" 
. 
. *Online Appendix 
. 
. *Survey Demographics
. 
. tab nationality

      {txt}D4. With what nationality do you {c |}
                    associate yourself {c |}      Freq.     Percent        Cum.
{hline 39}{c +}{hline 35}
                             Ukrainian {c |}{res}      1,870       93.13       93.13
{txt}                               Russian {c |}{res}         58        2.89       96.02
{txt}Ukrainian and Russian (only voluntary) {c |}{res}         12        0.60       96.61
{txt}                               Belarus {c |}{res}          3        0.15       96.76
{txt}                             Moldavian {c |}{res}          2        0.10       96.86
{txt}                             Bulgarian {c |}{res}          2        0.10       96.96
{txt}                              Romanian {c |}{res}          3        0.15       97.11
{txt}                                  Pole {c |}{res}          3        0.15       97.26
{txt}                                   Jew {c |}{res}          6        0.30       97.56
{txt}                                 Other {c |}{res}         32        1.59       99.15
{txt}                           HARD TO SAY {c |}{res}         17        0.85      100.00
{txt}{hline 39}{c +}{hline 35}
                                 Total {c |}{res}      2,008      100.00
{txt}
{com}. tab russpeaker

 {txt}russpeaker {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,759       87.60       87.60
{txt}          1 {c |}{res}        249       12.40      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,008      100.00
{txt}
{com}. tab female

     {txt}female {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        877       43.68       43.68
{txt}          1 {c |}{res}      1,131       56.32      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,008      100.00
{txt}
{com}. sum age education income

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}      2,008    52.64044    16.04743         18         92
{txt}{space 3}education {c |}{res}      2,007    6.662182    1.433637          1          8
{txt}{space 6}income {c |}{res}      1,982     2.85671    .8872038          1          5
{txt}
{com}. tab region

  {txt}4 Regions {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       West {c |}{res}        388       19.32       19.32
{txt}    Central {c |}{res}        856       42.63       61.95
{txt}      South {c |}{res}        498       24.80       86.75
{txt}       East {c |}{res}        266       13.25      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,008      100.00
{txt}
{com}. tab ownpets

   {txt}do you own pets or farm {c |}
                   animals {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                        No {c |}{res}        645       32.15       32.15
{txt}                      Pets {c |}{res}        987       49.20       81.36
{txt}              Farm Animals {c |}{res}         44        2.19       83.55
{txt}Both pets and farm animals {c |}{res}        330       16.45      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}      2,006      100.00
{txt}
{com}. tab employment

                         {txt}employment d6 {c |}      Freq.     Percent        Cum.
{hline 39}{c +}{hline 35}
                    Worker, farmworker {c |}{res}        234       11.73       11.73
{txt}    Servant (without higher education) {c |}{res}        148        7.42       19.15
{txt}  Professional (with higher education) {c |}{res}        434       21.75       40.90
{txt}       Self employed businesswomen/men {c |}{res}         91        4.56       45.46
{txt}                  Entrepreneur, farmer {c |}{res}        123        6.17       51.63
{txt}                      Military servant {c |}{res}         38        1.90       53.53
{txt}                           Householder {c |}{res}        141        7.07       60.60
{txt}Pension (because of age or disability) {c |}{res}        691       34.64       95.24
{txt}                               Student {c |}{res}         26        1.30       96.54
{txt}                            Unemployed {c |}{res}         69        3.46      100.00
{txt}{hline 39}{c +}{hline 35}
                                 Total {c |}{res}      1,995      100.00
{txt}
{com}. tab dsawviolencehuman

{txt}dsawviolenc {c |}
     ehuman {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,580       79.72       79.72
{txt}          1 {c |}{res}        402       20.28      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,982      100.00
{txt}
{com}. tab dsawviolenceanimals

{txt}dsawviolenc {c |}
   eanimals {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,809       91.04       91.04
{txt}          1 {c |}{res}        178        8.96      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,987      100.00
{txt}
{com}. tab dinjured

   {txt}dinjured {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,041       51.84       51.84
{txt}          1 {c |}{res}        967       48.16      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,008      100.00
{txt}
{com}. tab dlostanimals

{txt}dlostanimal {c |}
          s {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,712       85.26       85.26
{txt}          1 {c |}{res}        296       14.74      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,008      100.00
{txt}
{com}. tab ddisplaced

 {txt}ddisplaced {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,745       86.90       86.90
{txt}          1 {c |}{res}        263       13.10      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,008      100.00
{txt}
{com}. 
. *Experiment 1 
. 
. *Experiment 1 Balance Tests
. 
. iebaltab ownpets dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income employment urban_rural region4, groupvar(humanimaltxt) savexlsx(balance1)

{res}{phang}Balance table saved in Excel format to: {browse "balance1.xlsx":balance1.xlsx}{p_end}
{txt}
{com}. 
. *Experiment 1 Robustness Checks
. 
. *Experiment 1 – Human versus Animal Empathy (OLS Regression)
. 
. reg revempmediatorcheck ib3.humanimaltxt, robust

{txt}Linear regression                               Number of obs     = {res}     1,981
                                                {txt}F(2, 1978)        =  {res}     2.83
                                                {txt}Prob > F          = {res}    0.0592
                                                {txt}R-squared         = {res}    0.0029
                                                {txt}Root MSE          =    {res}  2.227

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revempmedi~k{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimaltxt {c |}
Treatment 1  {c |}{col 14}{res}{space 2} .2808695{col 26}{space 2} .1220683{col 37}{space 1}    2.30{col 46}{space 3}0.021{col 54}{space 4} .0414736{col 67}{space 3} .5202654
{txt}Treatment 2  {c |}{col 14}{res}{space 2} .0589415{col 26}{space 2}  .120005{col 37}{space 1}    0.49{col 46}{space 3}0.623{col 54}{space 4} -.176408{col 67}{space 3} .2942909
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 5.277008{col 26}{space 2} .0810668{col 37}{space 1}   65.09{col 46}{space 3}0.000{col 54}{space 4} 5.118023{col 67}{space 3} 5.435994
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg protectmoral ib3.humanimaltxt, robust

{txt}Linear regression                               Number of obs     = {res}     1,932
                                                {txt}F(2, 1929)        =  {res}     0.41
                                                {txt}Prob > F          = {res}    0.6646
                                                {txt}R-squared         = {res}    0.0004
                                                {txt}Root MSE          =    {res} 3.3946

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}protectmoral{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimaltxt {c |}
Treatment 1  {c |}{col 14}{res}{space 2} .0654156{col 26}{space 2} .1849632{col 37}{space 1}    0.35{col 46}{space 3}0.724{col 54}{space 4}-.2973333{col 67}{space 3} .4281644
{txt}Treatment 2  {c |}{col 14}{res}{space 2}-.1081186{col 26}{space 2} .1900418{col 37}{space 1}   -0.57{col 46}{space 3}0.569{col 54}{space 4}-.4808276{col 67}{space 3} .2645905
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 5.575107{col 26}{space 2} .1284157{col 37}{space 1}   43.41{col 46}{space 3}0.000{col 54}{space 4} 5.323259{col 67}{space 3} 5.826955
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg feelmorepain ib3.humanimaltxt, robust

{txt}Linear regression                               Number of obs     = {res}     1,938
                                                {txt}F(2, 1935)        =  {res}     0.48
                                                {txt}Prob > F          = {res}    0.6180
                                                {txt}R-squared         = {res}    0.0005
                                                {txt}Root MSE          =    {res} 3.5088

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}feelmorepain{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimaltxt {c |}
Treatment 1  {c |}{col 14}{res}{space 2} .0273301{col 26}{space 2} .1935847{col 37}{space 1}    0.14{col 46}{space 3}0.888{col 54}{space 4}-.3523265{col 67}{space 3} .4069868
{txt}Treatment 2  {c |}{col 14}{res}{space 2} .1787982{col 26}{space 2} .1923386{col 37}{space 1}    0.93{col 46}{space 3}0.353{col 54}{space 4}-.1984144{col 67}{space 3} .5560108
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 5.538244{col 26}{space 2}  .129899{col 37}{space 1}   42.63{col 46}{space 3}0.000{col 54}{space 4} 5.283487{col 67}{space 3}    5.793
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg protectmore ib3.humanimaltxt, robust

{txt}Linear regression                               Number of obs     = {res}     1,959
                                                {txt}F(2, 1956)        =  {res}     3.16
                                                {txt}Prob > F          = {res}    0.0427
                                                {txt}R-squared         = {res}    0.0032
                                                {txt}Root MSE          =    {res} 3.0967

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} protectmore{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimaltxt {c |}
Treatment 1  {c |}{col 14}{res}{space 2}  .169284{col 26}{space 2} .1722984{col 37}{space 1}    0.98{col 46}{space 3}0.326{col 54}{space 4}-.1686238{col 67}{space 3} .5071919
{txt}Treatment 2  {c |}{col 14}{res}{space 2} .4204073{col 26}{space 2} .1679175{col 37}{space 1}    2.50{col 46}{space 3}0.012{col 54}{space 4} .0910912{col 67}{space 3} .7497234
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 6.510519{col 26}{space 2} .1168801{col 37}{space 1}   55.70{col 46}{space 3}0.000{col 54}{space 4} 6.281296{col 67}{space 3} 6.739742
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Experiment 1 – Extended Controls (OLS Regression)
. 
. reg revempmediatorcheck  ib3.humanimaltxt i.ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4 , robust

{txt}Linear regression                               Number of obs     = {res}     1,909
                                                {txt}F(30, 1878)       =  {res}     3.48
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0551
                                                {txt}Root MSE          =    {res} 2.1727

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                    revempmediatorcheck{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      t{col 73}   P>|t|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}humanimaltxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2} .2639827{col 53}{space 2} .1231792{col 64}{space 1}    2.14{col 73}{space 3}0.032{col 81}{space 4} .0224002{col 94}{space 3} .5055652
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2} .0045918{col 53}{space 2} .1182389{col 64}{space 1}    0.04{col 73}{space 3}0.969{col 81}{space 4}-.2273016{col 94}{space 3} .2364853
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}-.6824389{col 53}{space 2} .1161686{col 64}{space 1}   -5.87{col 73}{space 3}0.000{col 81}{space 4} -.910272{col 94}{space 3}-.4546059
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2}-.4090973{col 53}{space 2} .4792383{col 64}{space 1}   -0.85{col 73}{space 3}0.393{col 81}{space 4}-1.348993{col 94}{space 3} .5307983
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2}-.5238872{col 53}{space 2}  .167044{col 64}{space 1}   -3.14{col 73}{space 3}0.002{col 81}{space 4}-.8514985{col 94}{space 3}-.1962759
{txt}{space 39} {c |}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2}-.1055569{col 53}{space 2} .1350807{col 64}{space 1}   -0.78{col 73}{space 3}0.435{col 81}{space 4}-.3704811{col 94}{space 3} .1593672
{txt}{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2}-.2074097{col 53}{space 2} .2027861{col 64}{space 1}   -1.02{col 73}{space 3}0.307{col 81}{space 4}-.6051195{col 94}{space 3} .1903001
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2}-.0539235{col 53}{space 2} .1017576{col 64}{space 1}   -0.53{col 73}{space 3}0.596{col 81}{space 4}-.2534933{col 94}{space 3} .1456463
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2}  .034192{col 53}{space 2} .1501883{col 64}{space 1}    0.23{col 73}{space 3}0.820{col 81}{space 4}-.2603614{col 94}{space 3} .3287454
{txt}{space 30}displaced {c |}{col 41}{res}{space 2} .0488024{col 53}{space 2} .1527424{col 64}{space 1}    0.32{col 73}{space 3}0.749{col 81}{space 4}-.2507603{col 94}{space 3} .3483651
{txt}{space 30}ukrainian {c |}{col 41}{res}{space 2} .1090456{col 53}{space 2} .2876902{col 64}{space 1}    0.38{col 73}{space 3}0.705{col 81}{space 4}-.4551805{col 94}{space 3} .6732717
{txt}{space 32}russian {c |}{col 41}{res}{space 2}-.1654689{col 53}{space 2} .4123664{col 64}{space 1}   -0.40{col 73}{space 3}0.688{col 81}{space 4}-.9742135{col 94}{space 3} .6432757
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2} .2741914{col 53}{space 2} .1785181{col 64}{space 1}    1.54{col 73}{space 3}0.125{col 81}{space 4}-.0759233{col 94}{space 3} .6243061
{txt}{space 33}female {c |}{col 41}{res}{space 2}-.4940991{col 53}{space 2} .1086656{col 64}{space 1}   -4.55{col 73}{space 3}0.000{col 81}{space 4}-.7072172{col 94}{space 3} -.280981
{txt}{space 36}age {c |}{col 41}{res}{space 2} .0084623{col 53}{space 2} .0047906{col 64}{space 1}    1.77{col 73}{space 3}0.077{col 81}{space 4}-.0009332{col 94}{space 3} .0178578
{txt}{space 30}education {c |}{col 41}{res}{space 2} .1023292{col 53}{space 2} .0420207{col 64}{space 1}    2.44{col 73}{space 3}0.015{col 81}{space 4}  .019917{col 94}{space 3} .1847415
{txt}{space 33}income {c |}{col 41}{res}{space 2} .1668143{col 53}{space 2}  .065989{col 64}{space 1}    2.53{col 73}{space 3}0.012{col 81}{space 4} .0373948{col 94}{space 3} .2962338
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2}-.2583759{col 53}{space 2} .2302812{col 64}{space 1}   -1.12{col 73}{space 3}0.262{col 81}{space 4}-.7100098{col 94}{space 3}  .193258
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2}   .01881{col 53}{space 2} .1908024{col 64}{space 1}    0.10{col 73}{space 3}0.921{col 81}{space 4}-.3553969{col 94}{space 3}  .393017
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2} .1772579{col 53}{space 2} .2693613{col 64}{space 1}    0.66{col 73}{space 3}0.511{col 81}{space 4}-.3510209{col 94}{space 3} .7055367
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2} .0869964{col 53}{space 2} .2582315{col 64}{space 1}    0.34{col 73}{space 3}0.736{col 81}{space 4}-.4194545{col 94}{space 3} .5934473
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2} .0088893{col 53}{space 2} .4176723{col 64}{space 1}    0.02{col 73}{space 3}0.983{col 81}{space 4}-.8102614{col 94}{space 3} .8280399
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2}-.0448402{col 53}{space 2} .2383883{col 64}{space 1}   -0.19{col 73}{space 3}0.851{col 81}{space 4} -.512374{col 94}{space 3} .4226937
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2}-.1884271{col 53}{space 2} .1999628{col 64}{space 1}   -0.94{col 73}{space 3}0.346{col 81}{space 4}-.5805998{col 94}{space 3} .2037456
{txt}{space 31}Student  {c |}{col 41}{res}{space 2} .3730713{col 53}{space 2} .4395693{col 64}{space 1}    0.85{col 73}{space 3}0.396{col 81}{space 4}-.4890243{col 94}{space 3} 1.235167
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2} .0733739{col 53}{space 2} .3294154{col 64}{space 1}    0.22{col 73}{space 3}0.824{col 81}{space 4}-.5726848{col 94}{space 3} .7194327
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2} .2197585{col 53}{space 2} .1442211{col 64}{space 1}    1.52{col 73}{space 3}0.128{col 81}{space 4} -.063092{col 94}{space 3}  .502609
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2} .2269594{col 53}{space 2} .1943892{col 64}{space 1}    1.17{col 73}{space 3}0.243{col 81}{space 4}-.1542821{col 94}{space 3}  .608201
{txt}{space 31}Central  {c |}{col 41}{res}{space 2} .1414313{col 53}{space 2} .1636603{col 64}{space 1}    0.86{col 73}{space 3}0.388{col 81}{space 4} -.179544{col 94}{space 3} .4624065
{txt}{space 33}South  {c |}{col 41}{res}{space 2} .0441962{col 53}{space 2} .1729607{col 64}{space 1}    0.26{col 73}{space 3}0.798{col 81}{space 4}-.2950192{col 94}{space 3} .3834116
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2}  3.97487{col 53}{space 2} .6400161{col 64}{space 1}    6.21{col 73}{space 3}0.000{col 81}{space 4} 2.719653{col 94}{space 3} 5.230088
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg protectmoral ib3.humanimaltxt i.ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4 , robust

{txt}Linear regression                               Number of obs     = {res}     1,854
                                                {txt}F(30, 1823)       =  {res}     4.52
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0661
                                                {txt}Root MSE          =    {res}  3.287

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                           protectmoral{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      t{col 73}   P>|t|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}humanimaltxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2} .1447544{col 53}{space 2}  .184798{col 64}{space 1}    0.78{col 73}{space 3}0.434{col 81}{space 4}-.2176837{col 94}{space 3} .5071924
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2}-.1134066{col 53}{space 2} .1881897{col 64}{space 1}   -0.60{col 73}{space 3}0.547{col 81}{space 4}-.4824967{col 94}{space 3} .2556835
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}-.6245399{col 53}{space 2} .1752403{col 64}{space 1}   -3.56{col 73}{space 3}0.000{col 81}{space 4}-.9682327{col 94}{space 3}-.2808471
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2} .4245281{col 53}{space 2} .5728077{col 64}{space 1}    0.74{col 73}{space 3}0.459{col 81}{space 4}-.6989003{col 94}{space 3} 1.547956
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2}-.1664622{col 53}{space 2} .2493278{col 64}{space 1}   -0.67{col 73}{space 3}0.504{col 81}{space 4}-.6554604{col 94}{space 3}  .322536
{txt}{space 39} {c |}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2} .3132284{col 53}{space 2}   .20901{col 64}{space 1}    1.50{col 73}{space 3}0.134{col 81}{space 4}-.0966958{col 94}{space 3} .7231527
{txt}{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2} -.363125{col 53}{space 2} .2868506{col 64}{space 1}   -1.27{col 73}{space 3}0.206{col 81}{space 4}-.9257153{col 94}{space 3} .1994653
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2}-.2662528{col 53}{space 2} .1557454{col 64}{space 1}   -1.71{col 73}{space 3}0.088{col 81}{space 4}-.5717109{col 94}{space 3} .0392053
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2} .1988057{col 53}{space 2}  .226929{col 64}{space 1}    0.88{col 73}{space 3}0.381{col 81}{space 4}-.2462624{col 94}{space 3} .6438739
{txt}{space 30}displaced {c |}{col 41}{res}{space 2} .2216598{col 53}{space 2} .2368077{col 64}{space 1}    0.94{col 73}{space 3}0.349{col 81}{space 4}-.2427831{col 94}{space 3} .6861026
{txt}{space 30}ukrainian {c |}{col 41}{res}{space 2}-.1841952{col 53}{space 2} .4330613{col 64}{space 1}   -0.43{col 73}{space 3}0.671{col 81}{space 4}-1.033544{col 94}{space 3} .6651533
{txt}{space 32}russian {c |}{col 41}{res}{space 2}-.5523129{col 53}{space 2} .6748396{col 64}{space 1}   -0.82{col 73}{space 3}0.413{col 81}{space 4}-1.875853{col 94}{space 3} .7712272
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2} -.120293{col 53}{space 2} .2645011{col 64}{space 1}   -0.45{col 73}{space 3}0.649{col 81}{space 4}-.6390502{col 94}{space 3} .3984641
{txt}{space 33}female {c |}{col 41}{res}{space 2}-.8510749{col 53}{space 2}  .160563{col 64}{space 1}   -5.30{col 73}{space 3}0.000{col 81}{space 4}-1.165982{col 94}{space 3}-.5361681
{txt}{space 36}age {c |}{col 41}{res}{space 2} .0321594{col 53}{space 2} .0070425{col 64}{space 1}    4.57{col 73}{space 3}0.000{col 81}{space 4} .0183472{col 94}{space 3} .0459715
{txt}{space 30}education {c |}{col 41}{res}{space 2}-.0765483{col 53}{space 2} .0636073{col 64}{space 1}   -1.20{col 73}{space 3}0.229{col 81}{space 4}-.2012992{col 94}{space 3} .0482026
{txt}{space 33}income {c |}{col 41}{res}{space 2}  .030701{col 53}{space 2} .0999382{col 64}{space 1}    0.31{col 73}{space 3}0.759{col 81}{space 4}-.1653045{col 94}{space 3} .2267065
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2}  .315425{col 53}{space 2} .3847452{col 64}{space 1}    0.82{col 73}{space 3}0.412{col 81}{space 4}-.4391626{col 94}{space 3} 1.070013
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2}-.2391751{col 53}{space 2} .2953779{col 64}{space 1}   -0.81{col 73}{space 3}0.418{col 81}{space 4}-.8184898{col 94}{space 3} .3401395
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2} .3776204{col 53}{space 2} .4175807{col 64}{space 1}    0.90{col 73}{space 3}0.366{col 81}{space 4}-.4413664{col 94}{space 3} 1.196607
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2}-.3170173{col 53}{space 2} .3922016{col 64}{space 1}   -0.81{col 73}{space 3}0.419{col 81}{space 4}-1.086229{col 94}{space 3} .4521945
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2} .0385847{col 53}{space 2} .6353287{col 64}{space 1}    0.06{col 73}{space 3}0.952{col 81}{space 4}-1.207464{col 94}{space 3} 1.284633
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2} .1543141{col 53}{space 2}  .393651{col 64}{space 1}    0.39{col 73}{space 3}0.695{col 81}{space 4}-.6177403{col 94}{space 3} .9263684
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2}  .208288{col 53}{space 2} .3096966{col 64}{space 1}    0.67{col 73}{space 3}0.501{col 81}{space 4}-.3991095{col 94}{space 3} .8156855
{txt}{space 31}Student  {c |}{col 41}{res}{space 2}-.2580801{col 53}{space 2} .7678997{col 64}{space 1}   -0.34{col 73}{space 3}0.737{col 81}{space 4}-1.764136{col 94}{space 3} 1.247976
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2} .2845445{col 53}{space 2}   .47704{col 64}{space 1}    0.60{col 73}{space 3}0.551{col 81}{space 4}-.6510578{col 94}{space 3} 1.220147
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2} .2439467{col 53}{space 2} .2137078{col 64}{space 1}    1.14{col 73}{space 3}0.254{col 81}{space 4}-.1751913{col 94}{space 3} .6630846
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2} .4352674{col 53}{space 2} .2981408{col 64}{space 1}    1.46{col 73}{space 3}0.144{col 81}{space 4}-.1494661{col 94}{space 3} 1.020001
{txt}{space 31}Central  {c |}{col 41}{res}{space 2} .1370248{col 53}{space 2} .2582571{col 64}{space 1}    0.53{col 73}{space 3}0.596{col 81}{space 4} -.369486{col 94}{space 3} .6435357
{txt}{space 33}South  {c |}{col 41}{res}{space 2} .3423988{col 53}{space 2} .2690977{col 64}{space 1}    1.27{col 73}{space 3}0.203{col 81}{space 4}-.1853733{col 94}{space 3}  .870171
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2} 4.549137{col 53}{space 2} .9358405{col 64}{space 1}    4.86{col 73}{space 3}0.000{col 81}{space 4} 2.713705{col 94}{space 3}  6.38457
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg feelmorepain ib3.humanimaltxt i.ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4 , robust

{txt}Linear regression                               Number of obs     = {res}     1,863
                                                {txt}F(30, 1832)       =  {res}     8.49
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1097
                                                {txt}Root MSE          =    {res} 3.3358

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                           feelmorepain{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      t{col 73}   P>|t|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}humanimaltxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2} .1336469{col 53}{space 2} .1905459{col 64}{space 1}    0.70{col 73}{space 3}0.483{col 81}{space 4}-.2400631{col 94}{space 3}  .507357
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2}  .121908{col 53}{space 2} .1846212{col 64}{space 1}    0.66{col 73}{space 3}0.509{col 81}{space 4}-.2401821{col 94}{space 3} .4839982
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}-.6952863{col 53}{space 2} .1826779{col 64}{space 1}   -3.81{col 73}{space 3}0.000{col 81}{space 4}-1.053565{col 94}{space 3}-.3370074
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2} .6588681{col 53}{space 2} .5421261{col 64}{space 1}    1.22{col 73}{space 3}0.224{col 81}{space 4} -.404382{col 94}{space 3} 1.722118
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2} .1762129{col 53}{space 2} .2509327{col 64}{space 1}    0.70{col 73}{space 3}0.483{col 81}{space 4}-.3159313{col 94}{space 3} .6683571
{txt}{space 39} {c |}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2} .2751731{col 53}{space 2} .2091337{col 64}{space 1}    1.32{col 73}{space 3}0.188{col 81}{space 4}-.1349923{col 94}{space 3} .6853385
{txt}{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2}-.4736671{col 53}{space 2} .2713642{col 64}{space 1}   -1.75{col 73}{space 3}0.081{col 81}{space 4}-1.005883{col 94}{space 3} .0585486
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2} -.336795{col 53}{space 2} .1580921{col 64}{space 1}   -2.13{col 73}{space 3}0.033{col 81}{space 4}-.6468547{col 94}{space 3}-.0267352
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2}-.0793013{col 53}{space 2}  .227173{col 64}{space 1}   -0.35{col 73}{space 3}0.727{col 81}{space 4}-.5248466{col 94}{space 3}  .366244
{txt}{space 30}displaced {c |}{col 41}{res}{space 2} .0968199{col 53}{space 2} .2512351{col 64}{space 1}    0.39{col 73}{space 3}0.700{col 81}{space 4}-.3959174{col 94}{space 3} .5895571
{txt}{space 30}ukrainian {c |}{col 41}{res}{space 2} .2136832{col 53}{space 2} .4310493{col 64}{space 1}    0.50{col 73}{space 3}0.620{col 81}{space 4}-.6317164{col 94}{space 3} 1.059083
{txt}{space 32}russian {c |}{col 41}{res}{space 2}  .212703{col 53}{space 2} .6138014{col 64}{space 1}    0.35{col 73}{space 3}0.729{col 81}{space 4}-.9911209{col 94}{space 3} 1.416527
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2}-.2497284{col 53}{space 2}   .26513{col 64}{space 1}   -0.94{col 73}{space 3}0.346{col 81}{space 4}-.7697171{col 94}{space 3} .2702603
{txt}{space 33}female {c |}{col 41}{res}{space 2}-.6244948{col 53}{space 2} .1679997{col 64}{space 1}   -3.72{col 73}{space 3}0.000{col 81}{space 4}-.9539859{col 94}{space 3}-.2950038
{txt}{space 36}age {c |}{col 41}{res}{space 2} .0452785{col 53}{space 2} .0071775{col 64}{space 1}    6.31{col 73}{space 3}0.000{col 81}{space 4} .0312016{col 94}{space 3} .0593555
{txt}{space 30}education {c |}{col 41}{res}{space 2}-.1123758{col 53}{space 2}  .061067{col 64}{space 1}   -1.84{col 73}{space 3}0.066{col 81}{space 4}-.2321441{col 94}{space 3} .0073925
{txt}{space 33}income {c |}{col 41}{res}{space 2}-.1342501{col 53}{space 2} .0996203{col 64}{space 1}   -1.35{col 73}{space 3}0.178{col 81}{space 4}-.3296314{col 94}{space 3} .0611311
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2} .2178925{col 53}{space 2} .3876198{col 64}{space 1}    0.56{col 73}{space 3}0.574{col 81}{space 4}-.5423305{col 94}{space 3} .9781156
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2} .2003435{col 53}{space 2} .3044288{col 64}{space 1}    0.66{col 73}{space 3}0.511{col 81}{space 4}-.3967204{col 94}{space 3} .7974075
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2}  .805206{col 53}{space 2} .4814445{col 64}{space 1}    1.67{col 73}{space 3}0.095{col 81}{space 4}-.1390318{col 94}{space 3} 1.749444
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2} .2121422{col 53}{space 2} .3983662{col 64}{space 1}    0.53{col 73}{space 3}0.594{col 81}{space 4}-.5691575{col 94}{space 3} .9934419
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2} .8322167{col 53}{space 2} .6828875{col 64}{space 1}    1.22{col 73}{space 3}0.223{col 81}{space 4} -.507103{col 94}{space 3} 2.171536
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2} .4166123{col 53}{space 2} .3898469{col 64}{space 1}    1.07{col 73}{space 3}0.285{col 81}{space 4}-.3479787{col 94}{space 3} 1.181203
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2} .6175165{col 53}{space 2} .3138294{col 64}{space 1}    1.97{col 73}{space 3}0.049{col 81}{space 4} .0020156{col 94}{space 3} 1.233017
{txt}{space 31}Student  {c |}{col 41}{res}{space 2} .7754243{col 53}{space 2} .7628191{col 64}{space 1}    1.02{col 73}{space 3}0.310{col 81}{space 4}-.7206621{col 94}{space 3} 2.271511
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2}   .30302{col 53}{space 2} .5034922{col 64}{space 1}    0.60{col 73}{space 3}0.547{col 81}{space 4} -.684459{col 94}{space 3} 1.290499
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2} .2033425{col 53}{space 2} .2219221{col 64}{space 1}    0.92{col 73}{space 3}0.360{col 81}{space 4}-.2319044{col 94}{space 3} .6385893
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2} .8342966{col 53}{space 2} .3042576{col 64}{space 1}    2.74{col 73}{space 3}0.006{col 81}{space 4} .2375684{col 94}{space 3} 1.431025
{txt}{space 31}Central  {c |}{col 41}{res}{space 2} .0883283{col 53}{space 2} .2753498{col 64}{space 1}    0.32{col 73}{space 3}0.748{col 81}{space 4}-.4517041{col 94}{space 3} .6283607
{txt}{space 33}South  {c |}{col 41}{res}{space 2}-.1181971{col 53}{space 2} .2802787{col 64}{space 1}   -0.42{col 73}{space 3}0.673{col 81}{space 4}-.6678965{col 94}{space 3} .4315023
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2}  4.01627{col 53}{space 2} .9471745{col 64}{space 1}    4.24{col 73}{space 3}0.000{col 81}{space 4} 2.158615{col 94}{space 3} 5.873925
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg protectmore ib3.humanimaltxt i.ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4 , robust

{txt}Linear regression                               Number of obs     = {res}     1,883
                                                {txt}F(30, 1852)       =  {res}     4.83
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0666
                                                {txt}Root MSE          =    {res} 3.0019

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                            protectmore{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      t{col 73}   P>|t|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}humanimaltxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2} .2590296{col 53}{space 2} .1705967{col 64}{space 1}    1.52{col 73}{space 3}0.129{col 81}{space 4}-.0755524{col 94}{space 3} .5936116
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2} .3614717{col 53}{space 2} .1668309{col 64}{space 1}    2.17{col 73}{space 3}0.030{col 81}{space 4} .0342754{col 94}{space 3}  .688668
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2} -.692556{col 53}{space 2} .1595433{col 64}{space 1}   -4.34{col 73}{space 3}0.000{col 81}{space 4} -1.00546{col 94}{space 3}-.3796523
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2} 1.271462{col 53}{space 2} .3747536{col 64}{space 1}    3.39{col 73}{space 3}0.001{col 81}{space 4} .5364782{col 94}{space 3} 2.006446
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2}-.2839454{col 53}{space 2} .2274561{col 64}{space 1}   -1.25{col 73}{space 3}0.212{col 81}{space 4}-.7300428{col 94}{space 3}  .162152
{txt}{space 39} {c |}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2}-.1128595{col 53}{space 2} .1799839{col 64}{space 1}   -0.63{col 73}{space 3}0.531{col 81}{space 4}-.4658521{col 94}{space 3} .2401331
{txt}{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2}-.4874986{col 53}{space 2} .2754065{col 64}{space 1}   -1.77{col 73}{space 3}0.077{col 81}{space 4}-1.027638{col 94}{space 3} .0526413
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2}-.0062892{col 53}{space 2} .1413416{col 64}{space 1}   -0.04{col 73}{space 3}0.965{col 81}{space 4}-.2834949{col 94}{space 3} .2709165
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2}  .042599{col 53}{space 2} .2034155{col 64}{space 1}    0.21{col 73}{space 3}0.834{col 81}{space 4}-.3563488{col 94}{space 3} .4415468
{txt}{space 30}displaced {c |}{col 41}{res}{space 2} .1774251{col 53}{space 2} .2264816{col 64}{space 1}    0.78{col 73}{space 3}0.433{col 81}{space 4} -.266761{col 94}{space 3} .6216113
{txt}{space 30}ukrainian {c |}{col 41}{res}{space 2}  .509149{col 53}{space 2}   .39515{col 64}{space 1}    1.29{col 73}{space 3}0.198{col 81}{space 4}-.2658372{col 94}{space 3} 1.284135
{txt}{space 32}russian {c |}{col 41}{res}{space 2} .0140836{col 53}{space 2} .5963776{col 64}{space 1}    0.02{col 73}{space 3}0.981{col 81}{space 4}-1.155559{col 94}{space 3} 1.183727
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2} .0016877{col 53}{space 2} .2426041{col 64}{space 1}    0.01{col 73}{space 3}0.994{col 81}{space 4}-.4741185{col 94}{space 3}  .477494
{txt}{space 33}female {c |}{col 41}{res}{space 2}-1.065387{col 53}{space 2} .1503578{col 64}{space 1}   -7.09{col 73}{space 3}0.000{col 81}{space 4}-1.360276{col 94}{space 3}-.7704985
{txt}{space 36}age {c |}{col 41}{res}{space 2} .0222399{col 53}{space 2} .0064378{col 64}{space 1}    3.45{col 73}{space 3}0.001{col 81}{space 4} .0096138{col 94}{space 3}  .034866
{txt}{space 30}education {c |}{col 41}{res}{space 2} .0377114{col 53}{space 2} .0553221{col 64}{space 1}    0.68{col 73}{space 3}0.496{col 81}{space 4}-.0707888{col 94}{space 3} .1462116
{txt}{space 33}income {c |}{col 41}{res}{space 2}-.1098554{col 53}{space 2} .0903112{col 64}{space 1}   -1.22{col 73}{space 3}0.224{col 81}{space 4}-.2869779{col 94}{space 3} .0672671
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2} .3455509{col 53}{space 2} .3453859{col 64}{space 1}    1.00{col 73}{space 3}0.317{col 81}{space 4}-.3318356{col 94}{space 3} 1.022937
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2} .1869895{col 53}{space 2} .2671902{col 64}{space 1}    0.70{col 73}{space 3}0.484{col 81}{space 4}-.3370362{col 94}{space 3} .7110152
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2} .5644841{col 53}{space 2} .4006284{col 64}{space 1}    1.41{col 73}{space 3}0.159{col 81}{space 4}-.2212466{col 94}{space 3} 1.350215
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2} .1224683{col 53}{space 2} .3498206{col 64}{space 1}    0.35{col 73}{space 3}0.726{col 81}{space 4}-.5636158{col 94}{space 3} .8085525
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2} .5407995{col 53}{space 2} .5925935{col 64}{space 1}    0.91{col 73}{space 3}0.362{col 81}{space 4}-.6214219{col 94}{space 3} 1.703021
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2} .2134146{col 53}{space 2} .3587695{col 64}{space 1}    0.59{col 73}{space 3}0.552{col 81}{space 4}-.4902207{col 94}{space 3} .9170498
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2} .0873474{col 53}{space 2} .2718536{col 64}{space 1}    0.32{col 73}{space 3}0.748{col 81}{space 4}-.4458243{col 94}{space 3} .6205191
{txt}{space 31}Student  {c |}{col 41}{res}{space 2}-.1062686{col 53}{space 2} .7361918{col 64}{space 1}   -0.14{col 73}{space 3}0.885{col 81}{space 4}-1.550122{col 94}{space 3} 1.337584
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2} .3272284{col 53}{space 2}  .419157{col 64}{space 1}    0.78{col 73}{space 3}0.435{col 81}{space 4}-.4948415{col 94}{space 3} 1.149298
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2} .1865393{col 53}{space 2}  .198576{col 64}{space 1}    0.94{col 73}{space 3}0.348{col 81}{space 4} -.202917{col 94}{space 3} .5759955
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2} .2119357{col 53}{space 2} .2680735{col 64}{space 1}    0.79{col 73}{space 3}0.429{col 81}{space 4}-.3138224{col 94}{space 3} .7376938
{txt}{space 31}Central  {c |}{col 41}{res}{space 2}-.2126274{col 53}{space 2} .2340797{col 64}{space 1}   -0.91{col 73}{space 3}0.364{col 81}{space 4}-.6717153{col 94}{space 3} .2464605
{txt}{space 33}South  {c |}{col 41}{res}{space 2}-.0777631{col 53}{space 2} .2457794{col 64}{space 1}   -0.32{col 73}{space 3}0.752{col 81}{space 4} -.559797{col 94}{space 3} .4042708
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2}   5.4177{col 53}{space 2} .8458685{col 64}{space 1}    6.40{col 73}{space 3}0.000{col 81}{space 4} 3.758744{col 94}{space 3} 7.076656
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Experiment 1 – Human versus Animal Empathy (Tobit Regression)
. 
. tobit revempmediatorcheck ib3.humanimaltxt, ll ul 
{res}{txt}
Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-4433.7698}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log likelihood = {res:-4433.7698}  
Iteration 1:{space 2}Log likelihood = {res:-4398.6018}  
Iteration 2:{space 2}Log likelihood = {res:-4398.1907}  
Iteration 3:{space 2}Log likelihood = {res: -4398.189}  
Iteration 4:{space 2}Log likelihood = {res: -4398.189}  
{res}
{txt}{col 1}Tobit regression{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:1,981}
{txt}{col 53}{ralign 17:Uncensored}{col 70} = {res}{ralign 6:1,693}
{txt}{col 1}Limits: Lower = {ralign 2:{res:0}}{col 53}{ralign 17:Left-censored}{col 70} = {res}{ralign 6:104}
{txt}{col 1}        Upper = {ralign 2:{res:10}}{col 53}{ralign 17:   Right-censored}{col 70} = {res}{ralign 6:184}

{txt}{col 53}{lalign 17:LR chi2({res:2})}{col 70} = {res}{ralign 6:5.61}
{txt}{col 53}{lalign 17:Prob > chi2}{col 70} = {res}{ralign 6:0.0604}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 9:-4398.189}{txt}{col 53}{lalign 17:Pseudo R2}{col 70} = {res}{ralign 6:0.0006}

{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       revempmediatorcheck{col 28}{c |} Coefficient{col 40}  Std. err.{col 52}      t{col 60}   P>|t|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}humanimaltxt {c |}
{space 14}Treatment 1  {c |}{col 28}{res}{space 2} .3207428{col 40}{space 2} .1420088{col 51}{space 1}    2.26{col 60}{space 3}0.024{col 68}{space 4} .0422403{col 81}{space 3} .5992452
{txt}{space 14}Treatment 2  {c |}{col 28}{res}{space 2} .0582391{col 40}{space 2} .1410389{col 51}{space 1}    0.41{col 60}{space 3}0.680{col 68}{space 4}-.2183611{col 81}{space 3} .3348394
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2} 5.311795{col 40}{space 2} .0965277{col 51}{space 1}   55.03{col 60}{space 3}0.000{col 68}{space 4} 5.122488{col 81}{space 3} 5.501101
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}var(e.revempmediatorcheck){c |}{col 28}{res}{space 2} 6.617368{col 40}{space 2} .2396345{col 68}{space 4} 6.163706{col 81}{space 3} 7.104421
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. tobit protectmoral ib3.humanimaltxt, ll ul
{res}{txt}
Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-4627.7334}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log likelihood = {res:-4627.7334}  
Iteration 1:{space 2}Log likelihood = {res:-4358.3752}  
Iteration 2:{space 2}Log likelihood = {res:-4352.3233}  
Iteration 3:{space 2}Log likelihood = {res:-4351.9393}  
Iteration 4:{space 2}Log likelihood = {res:-4351.9381}  
Iteration 5:{space 2}Log likelihood = {res:-4351.9381}  
{res}
{txt}{col 1}Tobit regression{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:1,932}
{txt}{col 53}{ralign 17:Uncensored}{col 70} = {res}{ralign 6:1,128}
{txt}{col 1}Limits: Lower = {ralign 2:{res:0}}{col 53}{ralign 17:Left-censored}{col 70} = {res}{ralign 6:316}
{txt}{col 1}        Upper = {ralign 2:{res:10}}{col 53}{ralign 17:   Right-censored}{col 70} = {res}{ralign 6:488}

{txt}{col 53}{lalign 17:LR chi2({res:2})}{col 70} = {res}{ralign 6:0.71}
{txt}{col 53}{lalign 17:Prob > chi2}{col 70} = {res}{ralign 6:0.7002}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-4351.9381}{txt}{col 53}{lalign 17:Pseudo R2}{col 70} = {res}{ralign 6:0.0001}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       protectmoral{col 21}{c |} Coefficient{col 33}  Std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}humanimaltxt {c |}
{space 7}Treatment 1  {c |}{col 21}{res}{space 2} .1520581{col 33}{space 2} .3184889{col 44}{space 1}    0.48{col 53}{space 3}0.633{col 61}{space 4}-.4725604{col 74}{space 3} .7766766
{txt}{space 7}Treatment 2  {c |}{col 21}{res}{space 2}-.1244104{col 33}{space 2} .3179727{col 44}{space 1}   -0.39{col 53}{space 3}0.696{col 61}{space 4}-.7480165{col 74}{space 3} .4991956
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}  5.89864{col 33}{space 2} .2181117{col 44}{space 1}   27.04{col 53}{space 3}0.000{col 61}{space 4} 5.470881{col 74}{space 3} 6.326399
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}var(e.protectmoral){c |}{col 21}{res}{space 2}  30.1947{col 33}{space 2} 1.452641{col 61}{space 4} 27.47606{col 74}{space 3} 33.18234
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. tobit feelmorepain ib3.humanimaltxt, ll ul 
{res}{txt}
Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:  -4622.56}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log likelihood = {res:  -4622.56}  
Iteration 1:{space 2}Log likelihood = {res:-4307.5946}  
Iteration 2:{space 2}Log likelihood = {res:-4302.6425}  
Iteration 3:{space 2}Log likelihood = {res:-4302.5889}  
Iteration 4:{space 2}Log likelihood = {res:-4302.5888}  
{res}
{txt}{col 1}Tobit regression{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:1,938}
{txt}{col 53}{ralign 17:Uncensored}{col 70} = {res}{ralign 6:1,071}
{txt}{col 1}Limits: Lower = {ralign 2:{res:0}}{col 53}{ralign 17:Left-censored}{col 70} = {res}{ralign 6:343}
{txt}{col 1}        Upper = {ralign 2:{res:10}}{col 53}{ralign 17:   Right-censored}{col 70} = {res}{ralign 6:524}

{txt}{col 53}{lalign 17:LR chi2({res:2})}{col 70} = {res}{ralign 6:1.07}
{txt}{col 53}{lalign 17:Prob > chi2}{col 70} = {res}{ralign 6:0.5870}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-4302.5888}{txt}{col 53}{lalign 17:Pseudo R2}{col 70} = {res}{ralign 6:0.0001}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       feelmorepain{col 21}{c |} Coefficient{col 33}  Std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}humanimaltxt {c |}
{space 7}Treatment 1  {c |}{col 21}{res}{space 2} .0630043{col 33}{space 2} .3470706{col 44}{space 1}    0.18{col 53}{space 3}0.856{col 61}{space 4} -.617667{col 74}{space 3} .7436757
{txt}{space 7}Treatment 2  {c |}{col 21}{res}{space 2}  .340027{col 33}{space 2} .3455259{col 44}{space 1}    0.98{col 53}{space 3}0.325{col 61}{space 4}-.3376148{col 74}{space 3} 1.017669
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}   5.8873{col 33}{space 2} .2361985{col 44}{space 1}   24.93{col 53}{space 3}0.000{col 61}{space 4}  5.42407{col 74}{space 3}  6.35053
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}var(e.feelmorepain){c |}{col 21}{res}{space 2} 35.49773{col 33}{space 2}  1.76802{col 61}{space 4} 32.19427{col 74}{space 3} 39.14015
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. tobit protectmore ib3.humanimaltxt, ll ul 
{res}{txt}
Refining starting values:

Grid node 0:{space 2}Log likelihood = {res: -4494.634}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log likelihood = {res: -4494.634}  
Iteration 1:{space 2}Log likelihood = {res:-4206.9395}  
Iteration 2:{space 2}Log likelihood = {res:-4185.1941}  
Iteration 3:{space 2}Log likelihood = {res:-4184.7161}  
Iteration 4:{space 2}Log likelihood = {res:-4184.7147}  
Iteration 5:{space 2}Log likelihood = {res:-4184.7147}  
{res}
{txt}{col 1}Tobit regression{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:1,959}
{txt}{col 53}{ralign 17:Uncensored}{col 70} = {res}{ralign 6:1,115}
{txt}{col 1}Limits: Lower = {ralign 2:{res:0}}{col 53}{ralign 17:Left-censored}{col 70} = {res}{ralign 6:154}
{txt}{col 1}        Upper = {ralign 2:{res:10}}{col 53}{ralign 17:   Right-censored}{col 70} = {res}{ralign 6:690}

{txt}{col 53}{lalign 17:LR chi2({res:2})}{col 70} = {res}{ralign 6:6.45}
{txt}{col 53}{lalign 17:Prob > chi2}{col 70} = {res}{ralign 6:0.0398}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-4184.7147}{txt}{col 53}{lalign 17:Pseudo R2}{col 70} = {res}{ralign 6:0.0008}

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       protectmore{col 20}{c |} Coefficient{col 32}  Std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}humanimaltxt {c |}
{space 6}Treatment 1  {c |}{col 20}{res}{space 2} .2501785{col 32}{space 2} .2948179{col 43}{space 1}    0.85{col 52}{space 3}0.396{col 60}{space 4}-.3280115{col 73}{space 3} .8283686
{txt}{space 6}Treatment 2  {c |}{col 20}{res}{space 2} .7357864{col 32}{space 2}  .292764{col 43}{space 1}    2.51{col 52}{space 3}0.012{col 60}{space 4} .1616243{col 73}{space 3} 1.309948
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2} 7.375173{col 32}{space 2} .2015501{col 43}{space 1}   36.59{col 52}{space 3}0.000{col 60}{space 4} 6.979897{col 73}{space 3} 7.770448
{txt}{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}var(e.protectmore){c |}{col 20}{res}{space 2} 25.58769{col 32}{space 2} 1.233026{col 60}{space 4} 23.28026{col 73}{space 3} 28.12382
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Experiment 1 – Extended Controls (Tobit Regression)
. 
. tobit revempmediatorcheck  ib3.humanimaltxt i.ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4 , ll ul 
{res}{txt}
Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-4215.9622}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log likelihood = {res:-4215.9622}  
Iteration 1:{space 2}Log likelihood = {res:-4182.7877}  
Iteration 2:{space 2}Log likelihood = {res:-4182.2239}  
Iteration 3:{space 2}Log likelihood = {res:-4182.2215}  
Iteration 4:{space 2}Log likelihood = {res:-4182.2215}  
{res}
{txt}{col 1}Tobit regression{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:1,909}
{txt}{col 53}{ralign 17:Uncensored}{col 70} = {res}{ralign 6:1,636}
{txt}{col 1}Limits: Lower = {ralign 2:{res:0}}{col 53}{ralign 17:Left-censored}{col 70} = {res}{ralign 6:98}
{txt}{col 1}        Upper = {ralign 2:{res:10}}{col 53}{ralign 17:   Right-censored}{col 70} = {res}{ralign 6:175}

{txt}{col 53}{lalign 17:LR chi2({res:30})}{col 70} = {res}{ralign 6:103.34}
{txt}{col 53}{lalign 17:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-4182.2215}{txt}{col 53}{lalign 17:Pseudo R2}{col 70} = {res}{ralign 6:0.0122}

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                    revempmediatorcheck{col 41}{c |} Coefficient{col 53}  Std. err.{col 65}      t{col 73}   P>|t|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}humanimaltxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2} .3036139{col 53}{space 2} .1405989{col 64}{space 1}    2.16{col 73}{space 3}0.031{col 81}{space 4} .0278675{col 94}{space 3} .5793603
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2}-.0026471{col 53}{space 2} .1389599{col 64}{space 1}   -0.02{col 73}{space 3}0.985{col 81}{space 4} -.275179{col 94}{space 3} .2698849
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}-.7780244{col 53}{space 2} .1336201{col 64}{space 1}   -5.82{col 73}{space 3}0.000{col 81}{space 4}-1.040084{col 94}{space 3} -.515965
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2}-.3702462{col 53}{space 2} .4168375{col 64}{space 1}   -0.89{col 73}{space 3}0.375{col 81}{space 4}-1.187759{col 94}{space 3} .4472669
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2}-.5810595{col 53}{space 2}  .195054{col 64}{space 1}   -2.98{col 73}{space 3}0.003{col 81}{space 4}-.9636048{col 94}{space 3}-.1985141
{txt}{space 39} {c |}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2}-.1291806{col 53}{space 2} .1545686{col 64}{space 1}   -0.84{col 73}{space 3}0.403{col 81}{space 4}-.4323247{col 94}{space 3} .1739636
{txt}{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2}-.2243785{col 53}{space 2} .2106422{col 64}{space 1}   -1.07{col 73}{space 3}0.287{col 81}{space 4}-.6374958{col 94}{space 3} .1887387
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2}-.0723632{col 53}{space 2} .1175604{col 64}{space 1}   -0.62{col 73}{space 3}0.538{col 81}{space 4}-.3029259{col 94}{space 3} .1581994
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2} .0201625{col 53}{space 2} .1695373{col 64}{space 1}    0.12{col 73}{space 3}0.905{col 81}{space 4}-.3123387{col 94}{space 3} .3526638
{txt}{space 30}displaced {c |}{col 41}{res}{space 2} .0516777{col 53}{space 2} .1850204{col 64}{space 1}    0.28{col 73}{space 3}0.780{col 81}{space 4}-.3111893{col 94}{space 3} .4145447
{txt}{space 30}ukrainian {c |}{col 41}{res}{space 2} .0697604{col 53}{space 2}   .30269{col 64}{space 1}    0.23{col 73}{space 3}0.818{col 81}{space 4}-.5238836{col 94}{space 3} .6634044
{txt}{space 32}russian {c |}{col 41}{res}{space 2}-.2703357{col 53}{space 2} .4624974{col 64}{space 1}   -0.58{col 73}{space 3}0.559{col 81}{space 4}-1.177398{col 94}{space 3} .6367268
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2} .3387541{col 53}{space 2} .1977091{col 64}{space 1}    1.71{col 73}{space 3}0.087{col 81}{space 4}-.0489983{col 94}{space 3} .7265066
{txt}{space 33}female {c |}{col 41}{res}{space 2}-.5606766{col 53}{space 2} .1250225{col 64}{space 1}   -4.48{col 73}{space 3}0.000{col 81}{space 4}-.8058741{col 94}{space 3}-.3154791
{txt}{space 36}age {c |}{col 41}{res}{space 2} .0099992{col 53}{space 2}  .005277{col 64}{space 1}    1.89{col 73}{space 3}0.058{col 81}{space 4}-.0003502{col 94}{space 3} .0203486
{txt}{space 30}education {c |}{col 41}{res}{space 2} .1148402{col 53}{space 2} .0452158{col 64}{space 1}    2.54{col 73}{space 3}0.011{col 81}{space 4} .0261617{col 94}{space 3} .2035187
{txt}{space 33}income {c |}{col 41}{res}{space 2} .1843184{col 53}{space 2} .0726936{col 64}{space 1}    2.54{col 73}{space 3}0.011{col 81}{space 4} .0417498{col 94}{space 3}  .326887
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2}-.2988297{col 53}{space 2} .2771452{col 64}{space 1}   -1.08{col 73}{space 3}0.281{col 81}{space 4}-.8423744{col 94}{space 3}  .244715
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2}  .024436{col 53}{space 2} .2223926{col 64}{space 1}    0.11{col 73}{space 3}0.913{col 81}{space 4}-.4117265{col 94}{space 3} .4605984
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2} .1659469{col 53}{space 2} .3220838{col 64}{space 1}    0.52{col 73}{space 3}0.606{col 81}{space 4}-.4657327{col 94}{space 3} .7976266
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2} .1339366{col 53}{space 2} .2942559{col 64}{space 1}    0.46{col 73}{space 3}0.649{col 81}{space 4} -.443166{col 94}{space 3} .7110393
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2} .0211058{col 53}{space 2} .4618464{col 64}{space 1}    0.05{col 73}{space 3}0.964{col 81}{space 4}-.8846799{col 94}{space 3} .9268915
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2}-.0208848{col 53}{space 2} .2817356{col 64}{space 1}   -0.07{col 73}{space 3}0.941{col 81}{space 4}-.5734324{col 94}{space 3} .5316628
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2}-.2257859{col 53}{space 2}  .227727{col 64}{space 1}   -0.99{col 73}{space 3}0.322{col 81}{space 4}-.6724102{col 94}{space 3} .2208385
{txt}{space 31}Student  {c |}{col 41}{res}{space 2} .4190689{col 53}{space 2} .5346611{col 64}{space 1}    0.78{col 73}{space 3}0.433{col 81}{space 4} -.629523{col 94}{space 3} 1.467661
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2} .0954571{col 53}{space 2}  .360669{col 64}{space 1}    0.26{col 73}{space 3}0.791{col 81}{space 4}-.6118967{col 94}{space 3}  .802811
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2} .2413874{col 53}{space 2} .1690242{col 64}{space 1}    1.43{col 73}{space 3}0.153{col 81}{space 4}-.0901076{col 94}{space 3} .5728823
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2}  .251663{col 53}{space 2} .2271292{col 64}{space 1}    1.11{col 73}{space 3}0.268{col 81}{space 4}-.1937889{col 94}{space 3} .6971149
{txt}{space 31}Central  {c |}{col 41}{res}{space 2} .1772365{col 53}{space 2} .1976254{col 64}{space 1}    0.90{col 73}{space 3}0.370{col 81}{space 4}-.2103517{col 94}{space 3} .5648248
{txt}{space 33}South  {c |}{col 41}{res}{space 2} .0606948{col 53}{space 2} .2031258{col 64}{space 1}    0.30{col 73}{space 3}0.765{col 81}{space 4}-.3376811{col 94}{space 3} .4590706
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2} 3.894997{col 53}{space 2} .6815303{col 64}{space 1}    5.72{col 73}{space 3}0.000{col 81}{space 4} 2.558361{col 94}{space 3} 5.231632
{txt}{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}var(e.revempmediatorcheck){c |}{col 41}{res}{space 2} 6.168941{col 53}{space 2} .2269773{col 81}{space 4} 5.739469{col 94}{space 3}  6.63055
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. tobit protectmoral ib3.humanimaltxt i.ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4 , ll ul 
{res}{txt}
Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-4385.9372}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log likelihood = {res:-4385.9372}  
Iteration 1:{space 2}Log likelihood = {res:-4136.4254}  
Iteration 2:{space 2}Log likelihood = {res:-4124.1318}  
Iteration 3:{space 2}Log likelihood = {res:-4124.0133}  
Iteration 4:{space 2}Log likelihood = {res:-4124.0132}  
{res}
{txt}{col 1}Tobit regression{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:1,854}
{txt}{col 53}{ralign 17:Uncensored}{col 70} = {res}{ralign 6:1,093}
{txt}{col 1}Limits: Lower = {ralign 2:{res:0}}{col 53}{ralign 17:Left-censored}{col 70} = {res}{ralign 6:300}
{txt}{col 1}        Upper = {ralign 2:{res:10}}{col 53}{ralign 17:   Right-censored}{col 70} = {res}{ralign 6:461}

{txt}{col 53}{lalign 17:LR chi2({res:30})}{col 70} = {res}{ralign 6:125.72}
{txt}{col 53}{lalign 17:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-4124.0132}{txt}{col 53}{lalign 17:Pseudo R2}{col 70} = {res}{ralign 6:0.0150}

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                           protectmoral{col 41}{c |} Coefficient{col 53}  Std. err.{col 65}      t{col 73}   P>|t|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}humanimaltxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2} .2938865{col 53}{space 2} .3109455{col 64}{space 1}    0.95{col 73}{space 3}0.345{col 81}{space 4}-.3159602{col 94}{space 3} .9037333
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2}-.1371304{col 53}{space 2} .3095304{col 64}{space 1}   -0.44{col 73}{space 3}0.658{col 81}{space 4}-.7442016{col 94}{space 3} .4699408
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}-1.038957{col 53}{space 2} .2956587{col 64}{space 1}   -3.51{col 73}{space 3}0.000{col 81}{space 4}-1.618822{col 94}{space 3}-.4590919
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2} .8489503{col 53}{space 2} .9601281{col 64}{space 1}    0.88{col 73}{space 3}0.377{col 81}{space 4}-1.034116{col 94}{space 3} 2.732016
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2}-.1989387{col 53}{space 2} .4325928{col 64}{space 1}   -0.46{col 73}{space 3}0.646{col 81}{space 4}-1.047368{col 94}{space 3} .6494906
{txt}{space 39} {c |}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2} .4372343{col 53}{space 2} .3434993{col 64}{space 1}    1.27{col 73}{space 3}0.203{col 81}{space 4}-.2364589{col 94}{space 3} 1.110928
{txt}{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2}-.5739609{col 53}{space 2} .4646113{col 64}{space 1}   -1.24{col 73}{space 3}0.217{col 81}{space 4}-1.485187{col 94}{space 3} .3372651
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2} -.452214{col 53}{space 2} .2608462{col 64}{space 1}   -1.73{col 73}{space 3}0.083{col 81}{space 4}-.9638026{col 94}{space 3} .0593745
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2}   .38452{col 53}{space 2} .3762226{col 64}{space 1}    1.02{col 73}{space 3}0.307{col 81}{space 4}-.3533524{col 94}{space 3} 1.122392
{txt}{space 30}displaced {c |}{col 41}{res}{space 2}   .36356{col 53}{space 2} .4064171{col 64}{space 1}    0.89{col 73}{space 3}0.371{col 81}{space 4}-.4335318{col 94}{space 3} 1.160652
{txt}{space 30}ukrainian {c |}{col 41}{res}{space 2}-.3987844{col 53}{space 2} .6889318{col 64}{space 1}   -0.58{col 73}{space 3}0.563{col 81}{space 4}-1.749962{col 94}{space 3} .9523937
{txt}{space 32}russian {c |}{col 41}{res}{space 2}-.7407211{col 53}{space 2}  1.04052{col 64}{space 1}   -0.71{col 73}{space 3}0.477{col 81}{space 4}-2.781457{col 94}{space 3} 1.300015
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2}-.1714165{col 53}{space 2} .4365656{col 64}{space 1}   -0.39{col 73}{space 3}0.695{col 81}{space 4}-1.027638{col 94}{space 3} .6848045
{txt}{space 33}female {c |}{col 41}{res}{space 2}-1.356861{col 53}{space 2} .2774197{col 64}{space 1}   -4.89{col 73}{space 3}0.000{col 81}{space 4}-1.900955{col 94}{space 3}-.8127675
{txt}{space 36}age {c |}{col 41}{res}{space 2} .0517999{col 53}{space 2}  .011707{col 64}{space 1}    4.42{col 73}{space 3}0.000{col 81}{space 4} .0288393{col 94}{space 3} .0747604
{txt}{space 30}education {c |}{col 41}{res}{space 2}-.1364044{col 53}{space 2} .1003133{col 64}{space 1}   -1.36{col 73}{space 3}0.174{col 81}{space 4}-.3331453{col 94}{space 3} .0603366
{txt}{space 33}income {c |}{col 41}{res}{space 2}  .032108{col 53}{space 2} .1637133{col 64}{space 1}    0.20{col 73}{space 3}0.845{col 81}{space 4}-.2889772{col 94}{space 3} .3531932
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2}  .509625{col 53}{space 2} .6131558{col 64}{space 1}    0.83{col 73}{space 3}0.406{col 81}{space 4}-.6929362{col 94}{space 3} 1.712186
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2}-.4617785{col 53}{space 2} .4898764{col 64}{space 1}   -0.94{col 73}{space 3}0.346{col 81}{space 4}-1.422556{col 94}{space 3} .4989992
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2} .6024386{col 53}{space 2} .7011223{col 64}{space 1}    0.86{col 73}{space 3}0.390{col 81}{space 4}-.7726484{col 94}{space 3} 1.977526
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2}-.4631048{col 53}{space 2}  .643307{col 64}{space 1}   -0.72{col 73}{space 3}0.472{col 81}{space 4}-1.724801{col 94}{space 3} .7985909
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2} .0201296{col 53}{space 2} 1.010733{col 64}{space 1}    0.02{col 73}{space 3}0.984{col 81}{space 4}-1.962187{col 94}{space 3} 2.002446
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2} .1508777{col 53}{space 2} .6294175{col 64}{space 1}    0.24{col 73}{space 3}0.811{col 81}{space 4}-1.083577{col 94}{space 3} 1.385332
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2} .3743847{col 53}{space 2} .5035327{col 64}{space 1}    0.74{col 73}{space 3}0.457{col 81}{space 4}-.6131766{col 94}{space 3} 1.361946
{txt}{space 31}Student  {c |}{col 41}{res}{space 2}-.6198848{col 53}{space 2} 1.171968{col 64}{space 1}   -0.53{col 73}{space 3}0.597{col 81}{space 4}-2.918424{col 94}{space 3} 1.678655
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2} .4615367{col 53}{space 2} .7915373{col 64}{space 1}    0.58{col 73}{space 3}0.560{col 81}{space 4}-1.090878{col 94}{space 3} 2.013951
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2} .3794188{col 53}{space 2} .3760074{col 64}{space 1}    1.01{col 73}{space 3}0.313{col 81}{space 4}-.3580316{col 94}{space 3} 1.116869
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2}  .697918{col 53}{space 2} .5009969{col 64}{space 1}    1.39{col 73}{space 3}0.164{col 81}{space 4}-.2846698{col 94}{space 3} 1.680506
{txt}{space 31}Central  {c |}{col 41}{res}{space 2} .1924063{col 53}{space 2}  .433255{col 64}{space 1}    0.44{col 73}{space 3}0.657{col 81}{space 4}-.6573217{col 94}{space 3} 1.042134
{txt}{space 33}South  {c |}{col 41}{res}{space 2} .4820044{col 53}{space 2} .4479467{col 64}{space 1}    1.08{col 73}{space 3}0.282{col 81}{space 4} -.396538{col 94}{space 3} 1.360547
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2} 4.516875{col 53}{space 2} 1.523746{col 64}{space 1}    2.96{col 73}{space 3}0.003{col 81}{space 4} 1.528406{col 94}{space 3} 7.505344
{txt}{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}var(e.protectmoral){c |}{col 41}{res}{space 2} 27.21183{col 53}{space 2} 1.324828{col 81}{space 4} 24.73369{col 94}{space 3} 29.93827
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. tobit feelmorepain ib3.humanimaltxt i.ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4 , ll ul 
{res}{txt}
Refining starting values:

Grid node 0:{space 2}Log likelihood = {res: -4345.066}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log likelihood = {res: -4345.066}  
Iteration 1:{space 2}Log likelihood = {res:-4053.0049}  
Iteration 2:{space 2}Log likelihood = {res:-4030.3461}  
Iteration 3:{space 2}Log likelihood = {res:-4029.7348}  
Iteration 4:{space 2}Log likelihood = {res:-4029.7344}  
{res}
{txt}{col 1}Tobit regression{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:1,863}
{txt}{col 53}{ralign 17:Uncensored}{col 70} = {res}{ralign 6:1,032}
{txt}{col 1}Limits: Lower = {ralign 2:{res:0}}{col 53}{ralign 17:Left-censored}{col 70} = {res}{ralign 6:331}
{txt}{col 1}        Upper = {ralign 2:{res:10}}{col 53}{ralign 17:   Right-censored}{col 70} = {res}{ralign 6:500}

{txt}{col 53}{lalign 17:LR chi2({res:30})}{col 70} = {res}{ralign 6:221.86}
{txt}{col 53}{lalign 17:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-4029.7344}{txt}{col 53}{lalign 17:Pseudo R2}{col 70} = {res}{ralign 6:0.0268}

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                           feelmorepain{col 41}{c |} Coefficient{col 53}  Std. err.{col 65}      t{col 73}   P>|t|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}humanimaltxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2} .2375192{col 53}{space 2} .3340951{col 64}{space 1}    0.71{col 73}{space 3}0.477{col 81}{space 4}-.4177278{col 94}{space 3} .8927663
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2} .2430798{col 53}{space 2} .3322696{col 64}{space 1}    0.73{col 73}{space 3}0.465{col 81}{space 4}-.4085871{col 94}{space 3} .8947466
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}-1.265521{col 53}{space 2} .3187002{col 64}{space 1}   -3.97{col 73}{space 3}0.000{col 81}{space 4}-1.890575{col 94}{space 3}-.6404677
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2} 1.322513{col 53}{space 2} 1.008528{col 64}{space 1}    1.31{col 73}{space 3}0.190{col 81}{space 4}-.6554725{col 94}{space 3} 3.300498
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2} .3628927{col 53}{space 2} .4641442{col 64}{space 1}    0.78{col 73}{space 3}0.434{col 81}{space 4}-.5474143{col 94}{space 3}   1.2732
{txt}{space 39} {c |}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2} .4280955{col 53}{space 2} .3677085{col 64}{space 1}    1.16{col 73}{space 3}0.244{col 81}{space 4}-.2930761{col 94}{space 3} 1.149267
{txt}{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2}-.6929353{col 53}{space 2} .4944552{col 64}{space 1}   -1.40{col 73}{space 3}0.161{col 81}{space 4} -1.66269{col 94}{space 3} .2768194
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2}-.6644499{col 53}{space 2} .2800179{col 64}{space 1}   -2.37{col 73}{space 3}0.018{col 81}{space 4}-1.213637{col 94}{space 3}-.1152624
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2}-.1108739{col 53}{space 2} .4012109{col 64}{space 1}   -0.28{col 73}{space 3}0.782{col 81}{space 4}-.8977524{col 94}{space 3} .6760047
{txt}{space 30}displaced {c |}{col 41}{res}{space 2} .1736128{col 53}{space 2} .4414764{col 64}{space 1}    0.39{col 73}{space 3}0.694{col 81}{space 4}-.6922367{col 94}{space 3} 1.039462
{txt}{space 30}ukrainian {c |}{col 41}{res}{space 2} .4483315{col 53}{space 2}  .731964{col 64}{space 1}    0.61{col 73}{space 3}0.540{col 81}{space 4}-.9872395{col 94}{space 3} 1.883902
{txt}{space 32}russian {c |}{col 41}{res}{space 2} .6065711{col 53}{space 2} 1.093583{col 64}{space 1}    0.55{col 73}{space 3}0.579{col 81}{space 4}-1.538229{col 94}{space 3} 2.751371
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2}-.4739209{col 53}{space 2} .4710326{col 64}{space 1}   -1.01{col 73}{space 3}0.314{col 81}{space 4}-1.397738{col 94}{space 3} .4498961
{txt}{space 33}female {c |}{col 41}{res}{space 2}-1.124669{col 53}{space 2} .2998685{col 64}{space 1}   -3.75{col 73}{space 3}0.000{col 81}{space 4}-1.712789{col 94}{space 3} -.536549
{txt}{space 36}age {c |}{col 41}{res}{space 2} .0772939{col 53}{space 2} .0125972{col 64}{space 1}    6.14{col 73}{space 3}0.000{col 81}{space 4} .0525876{col 94}{space 3} .1020002
{txt}{space 30}education {c |}{col 41}{res}{space 2}-.2362459{col 53}{space 2} .1078681{col 64}{space 1}   -2.19{col 73}{space 3}0.029{col 81}{space 4}-.4478031{col 94}{space 3}-.0246887
{txt}{space 33}income {c |}{col 41}{res}{space 2}-.2794181{col 53}{space 2} .1741193{col 64}{space 1}   -1.60{col 73}{space 3}0.109{col 81}{space 4}-.6209112{col 94}{space 3} .0620749
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2} .3921543{col 53}{space 2} .6624025{col 64}{space 1}    0.59{col 73}{space 3}0.554{col 81}{space 4}-.9069887{col 94}{space 3} 1.691297
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2}  .307767{col 53}{space 2} .5272335{col 64}{space 1}    0.58{col 73}{space 3}0.559{col 81}{space 4}-.7262744{col 94}{space 3} 1.341808
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2} 1.379189{col 53}{space 2} .7770132{col 64}{space 1}    1.77{col 73}{space 3}0.076{col 81}{space 4}-.1447356{col 94}{space 3} 2.903113
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2} .2351324{col 53}{space 2} .6929426{col 64}{space 1}    0.34{col 73}{space 3}0.734{col 81}{space 4}-1.123907{col 94}{space 3} 1.594172
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2} 1.697662{col 53}{space 2} 1.113821{col 64}{space 1}    1.52{col 73}{space 3}0.128{col 81}{space 4}-.4868306{col 94}{space 3} 3.882154
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2} .5429008{col 53}{space 2} .6688604{col 64}{space 1}    0.81{col 73}{space 3}0.417{col 81}{space 4}-.7689077{col 94}{space 3} 1.854709
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2} 1.038189{col 53}{space 2} .5427382{col 64}{space 1}    1.91{col 73}{space 3}0.056{col 81}{space 4}-.0262608{col 94}{space 3}  2.10264
{txt}{space 31}Student  {c |}{col 41}{res}{space 2} 1.247032{col 53}{space 2} 1.255475{col 64}{space 1}    0.99{col 73}{space 3}0.321{col 81}{space 4} -1.21528{col 94}{space 3} 3.709343
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2} .5670095{col 53}{space 2} .8570258{col 64}{space 1}    0.66{col 73}{space 3}0.508{col 81}{space 4} -1.11384{col 94}{space 3} 2.247859
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2} .3831819{col 53}{space 2} .4043346{col 64}{space 1}    0.95{col 73}{space 3}0.343{col 81}{space 4}-.4098231{col 94}{space 3} 1.176187
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2} 1.581319{col 53}{space 2} .5430096{col 64}{space 1}    2.91{col 73}{space 3}0.004{col 81}{space 4} .5163364{col 94}{space 3} 2.646301
{txt}{space 31}Central  {c |}{col 41}{res}{space 2} .2228752{col 53}{space 2} .4704558{col 64}{space 1}    0.47{col 73}{space 3}0.636{col 81}{space 4}-.6998104{col 94}{space 3} 1.145561
{txt}{space 33}South  {c |}{col 41}{res}{space 2}-.2205664{col 53}{space 2} .4834825{col 64}{space 1}   -0.46{col 73}{space 3}0.648{col 81}{space 4}-1.168801{col 94}{space 3}  .727668
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2} 3.668641{col 53}{space 2} 1.634506{col 64}{space 1}    2.24{col 73}{space 3}0.025{col 81}{space 4} .4629508{col 94}{space 3}  6.87433
{txt}{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}var(e.feelmorepain){c |}{col 41}{res}{space 2} 30.97539{col 53}{space 2} 1.564126{col 81}{space 4} 28.05474{col 94}{space 3} 34.20009
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. tobit protectmore ib3.humanimaltxt i.ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4 , ll ul 
{res}{txt}
Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-4268.1117}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log likelihood = {res:-4268.1117}  
Iteration 1:{space 2}Log likelihood = {res:-3997.4606}  
Iteration 2:{space 2}Log likelihood = {res:-3975.0932}  
Iteration 3:{space 2}Log likelihood = {res: -3974.457}  
Iteration 4:{space 2}Log likelihood = {res:-3974.4549}  
Iteration 5:{space 2}Log likelihood = {res:-3974.4549}  
{res}
{txt}{col 1}Tobit regression{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:1,883}
{txt}{col 53}{ralign 17:Uncensored}{col 70} = {res}{ralign 6:1,086}
{txt}{col 1}Limits: Lower = {ralign 2:{res:0}}{col 53}{ralign 17:Left-censored}{col 70} = {res}{ralign 6:145}
{txt}{col 1}        Upper = {ralign 2:{res:10}}{col 53}{ralign 17:   Right-censored}{col 70} = {res}{ralign 6:652}

{txt}{col 53}{lalign 17:LR chi2({res:30})}{col 70} = {res}{ralign 6:135.55}
{txt}{col 53}{lalign 17:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-3974.4549}{txt}{col 53}{lalign 17:Pseudo R2}{col 70} = {res}{ralign 6:0.0168}

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                            protectmore{col 41}{c |} Coefficient{col 53}  Std. err.{col 65}      t{col 73}   P>|t|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}humanimaltxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2} .3980946{col 53}{space 2} .2867713{col 64}{space 1}    1.39{col 73}{space 3}0.165{col 81}{space 4}-.1643342{col 94}{space 3} .9605234
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2} .6245102{col 53}{space 2}  .284255{col 64}{space 1}    2.20{col 73}{space 3}0.028{col 81}{space 4} .0670165{col 94}{space 3} 1.182004
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}-1.232421{col 53}{space 2} .2737426{col 64}{space 1}   -4.50{col 73}{space 3}0.000{col 81}{space 4}-1.769297{col 94}{space 3}-.6955444
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2} 2.701558{col 53}{space 2} .9379213{col 64}{space 1}    2.88{col 73}{space 3}0.004{col 81}{space 4} .8620647{col 94}{space 3} 4.541052
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2}-.5913702{col 53}{space 2} .3980586{col 64}{space 1}   -1.49{col 73}{space 3}0.138{col 81}{space 4}-1.372061{col 94}{space 3} .1893203
{txt}{space 39} {c |}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2}-.2988335{col 53}{space 2} .3137416{col 64}{space 1}   -0.95{col 73}{space 3}0.341{col 81}{space 4}-.9141576{col 94}{space 3} .3164907
{txt}{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2}-.7433013{col 53}{space 2} .4257059{col 64}{space 1}   -1.75{col 73}{space 3}0.081{col 81}{space 4}-1.578215{col 94}{space 3} .0916124
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2}-.0514307{col 53}{space 2} .2400074{col 64}{space 1}   -0.21{col 73}{space 3}0.830{col 81}{space 4} -.522144{col 94}{space 3} .4192826
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2} .0218816{col 53}{space 2} .3453135{col 64}{space 1}    0.06{col 73}{space 3}0.949{col 81}{space 4}-.6553628{col 94}{space 3} .6991261
{txt}{space 30}displaced {c |}{col 41}{res}{space 2} .2241375{col 53}{space 2} .3776076{col 64}{space 1}    0.59{col 73}{space 3}0.553{col 81}{space 4}-.5164436{col 94}{space 3} .9647186
{txt}{space 30}ukrainian {c |}{col 41}{res}{space 2} .9277405{col 53}{space 2} .6226824{col 64}{space 1}    1.49{col 73}{space 3}0.136{col 81}{space 4}-.2934922{col 94}{space 3} 2.148973
{txt}{space 32}russian {c |}{col 41}{res}{space 2} .0391639{col 53}{space 2} .9334733{col 64}{space 1}    0.04{col 73}{space 3}0.967{col 81}{space 4}-1.791606{col 94}{space 3} 1.869934
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2} .0164009{col 53}{space 2} .4016378{col 64}{space 1}    0.04{col 73}{space 3}0.967{col 81}{space 4}-.7713092{col 94}{space 3}  .804111
{txt}{space 33}female {c |}{col 41}{res}{space 2}-1.772584{col 53}{space 2} .2563726{col 64}{space 1}   -6.91{col 73}{space 3}0.000{col 81}{space 4}-2.275394{col 94}{space 3}-1.269775
{txt}{space 36}age {c |}{col 41}{res}{space 2} .0397862{col 53}{space 2} .0106833{col 64}{space 1}    3.72{col 73}{space 3}0.000{col 81}{space 4} .0188337{col 94}{space 3} .0607388
{txt}{space 30}education {c |}{col 41}{res}{space 2} .0338983{col 53}{space 2} .0926698{col 64}{space 1}    0.37{col 73}{space 3}0.715{col 81}{space 4}-.1478499{col 94}{space 3} .2156464
{txt}{space 33}income {c |}{col 41}{res}{space 2}-.2338276{col 53}{space 2} .1481552{col 64}{space 1}   -1.58{col 73}{space 3}0.115{col 81}{space 4}-.5243962{col 94}{space 3}  .056741
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2} .5360211{col 53}{space 2} .5670526{col 64}{space 1}    0.95{col 73}{space 3}0.345{col 81}{space 4} -.576108{col 94}{space 3}  1.64815
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2} .2055635{col 53}{space 2} .4514888{col 64}{space 1}    0.46{col 73}{space 3}0.649{col 81}{space 4}-.6799167{col 94}{space 3} 1.091044
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2} .7104767{col 53}{space 2} .6571318{col 64}{space 1}    1.08{col 73}{space 3}0.280{col 81}{space 4}-.5783198{col 94}{space 3} 1.999273
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2} .1553844{col 53}{space 2} .5958257{col 64}{space 1}    0.26{col 73}{space 3}0.794{col 81}{space 4}-1.013176{col 94}{space 3} 1.323945
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2} 1.027424{col 53}{space 2} .9672534{col 64}{space 1}    1.06{col 73}{space 3}0.288{col 81}{space 4} -.869597{col 94}{space 3} 2.924445
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2} .2804592{col 53}{space 2} .5783021{col 64}{space 1}    0.48{col 73}{space 3}0.628{col 81}{space 4}-.8537329{col 94}{space 3} 1.414651
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2} .0372292{col 53}{space 2} .4637416{col 64}{space 1}    0.08{col 73}{space 3}0.936{col 81}{space 4}-.8722816{col 94}{space 3} .9467401
{txt}{space 31}Student  {c |}{col 41}{res}{space 2}-.0519102{col 53}{space 2} 1.090985{col 64}{space 1}   -0.05{col 73}{space 3}0.962{col 81}{space 4}  -2.1916{col 94}{space 3} 2.087779
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2} .5314723{col 53}{space 2} .7309671{col 64}{space 1}    0.73{col 73}{space 3}0.467{col 81}{space 4}-.9021332{col 94}{space 3} 1.965078
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2} .3351809{col 53}{space 2} .3467349{col 64}{space 1}    0.97{col 73}{space 3}0.334{col 81}{space 4}-.3448513{col 94}{space 3} 1.015213
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2} .3643472{col 53}{space 2}  .466755{col 64}{space 1}    0.78{col 73}{space 3}0.435{col 81}{space 4}-.5510737{col 94}{space 3} 1.279768
{txt}{space 31}Central  {c |}{col 41}{res}{space 2}-.3934065{col 53}{space 2} .4031821{col 64}{space 1}   -0.98{col 73}{space 3}0.329{col 81}{space 4}-1.184145{col 94}{space 3} .3973323
{txt}{space 33}South  {c |}{col 41}{res}{space 2}-.1376993{col 53}{space 2} .4151156{col 64}{space 1}   -0.33{col 73}{space 3}0.740{col 81}{space 4}-.9518427{col 94}{space 3} .6764441
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2} 5.889678{col 53}{space 2} 1.392779{col 64}{space 1}    4.23{col 73}{space 3}0.000{col 81}{space 4} 3.158097{col 94}{space 3} 8.621259
{txt}{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}var(e.protectmore){c |}{col 41}{res}{space 2} 22.91776{col 53}{space 2} 1.113533{col 81}{space 4} 20.83467{col 94}{space 3} 25.20911
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Experiment 1 – Human versus Animal Empathy (Ordered Probit Regression)
. 
. oprobit revempmediatorcheck ib3.humanimaltxt, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-3020.3093}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-3017.2822}  
Iteration 2:{space 2}Log pseudolikelihood = {res: -3017.282}  
{res}
{txt}{col 1}Ordered probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,981}
{txt}{col 57}{lalign 13:Wald chi2({res:2})}{col 70} = {res}{ralign 6:5.99}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0501}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 9:-3017.282}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0010}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}revempmediatorcheck{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      z{col 53}   P>|z|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}humanimaltxt {c |}
{space 7}Treatment 1  {c |}{col 21}{res}{space 2}  .143249{col 33}{space 2} .0597635{col 44}{space 1}    2.40{col 53}{space 3}0.017{col 61}{space 4} .0261147{col 74}{space 3} .2603834
{txt}{space 7}Treatment 2  {c |}{col 21}{res}{space 2} .0386944{col 33}{space 2} .0589709{col 44}{space 1}    0.66{col 53}{space 3}0.512{col 61}{space 4}-.0768864{col 74}{space 3} .1542751
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}/cut1 {c |}{col 21}{res}{space 2}-1.566037{col 33}{space 2}  .055237{col 61}{space 4}-1.674299{col 74}{space 3}-1.457775
{txt}{space 14}/cut2 {c |}{col 21}{res}{space 2}-1.499065{col 33}{space 2} .0538848{col 61}{space 4}-1.604678{col 74}{space 3}-1.393453
{txt}{space 14}/cut3 {c |}{col 21}{res}{space 2}-1.289515{col 33}{space 2} .0496194{col 61}{space 4}-1.386767{col 74}{space 3}-1.192263
{txt}{space 14}/cut4 {c |}{col 21}{res}{space 2}-1.141936{col 33}{space 2} .0477083{col 61}{space 4}-1.235443{col 74}{space 3}-1.048429
{txt}{space 14}/cut5 {c |}{col 21}{res}{space 2}-1.044005{col 33}{space 2} .0467947{col 61}{space 4}-1.135721{col 74}{space 3}-.9522895
{txt}{space 14}/cut6 {c |}{col 21}{res}{space 2} .6864484{col 33}{space 2} .0442926{col 61}{space 4} .5996364{col 74}{space 3} .7732604
{txt}{space 14}/cut7 {c |}{col 21}{res}{space 2} .8426966{col 33}{space 2} .0452784{col 61}{space 4} .7539525{col 74}{space 3} .9314407
{txt}{space 14}/cut8 {c |}{col 21}{res}{space 2} 1.096187{col 33}{space 2} .0475058{col 61}{space 4} 1.003077{col 74}{space 3} 1.189297
{txt}{space 14}/cut9 {c |}{col 21}{res}{space 2} 1.270301{col 33}{space 2} .0494484{col 61}{space 4} 1.173384{col 74}{space 3} 1.367219
{txt}{space 13}/cut10 {c |}{col 21}{res}{space 2} 1.383458{col 33}{space 2} .0514373{col 61}{space 4} 1.282643{col 74}{space 3} 1.484273
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. oprobit protectmoral ib3.humanimaltxt, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-3347.1511}  
Iteration 1:{space 2}Log pseudolikelihood = {res: -3346.816}  
Iteration 2:{space 2}Log pseudolikelihood = {res: -3346.816}  
{res}
{txt}{col 1}Ordered probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,932}
{txt}{col 57}{lalign 13:Wald chi2({res:2})}{col 70} = {res}{ralign 6:0.66}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.7174}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 9:-3346.816}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0001}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}protectmoral{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimaltxt {c |}
Treatment 1  {c |}{col 14}{res}{space 2} .0172137{col 26}{space 2} .0583027{col 37}{space 1}    0.30{col 46}{space 3}0.768{col 54}{space 4}-.0970575{col 67}{space 3} .1314849
{txt}Treatment 2  {c |}{col 14}{res}{space 2}-.0318656{col 26}{space 2}  .059964{col 37}{space 1}   -0.53{col 46}{space 3}0.595{col 54}{space 4}-.1493928{col 67}{space 3} .0856616
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-.9849837{col 26}{space 2} .0477315{col 54}{space 4}-1.078536{col 67}{space 3}-.8914316
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-.9139939{col 26}{space 2}  .046947{col 54}{space 4}-1.006008{col 67}{space 3}-.8219795
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2}-.8547323{col 26}{space 2} .0462877{col 54}{space 4}-.9454545{col 67}{space 3}-.7640102
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2}-.7563168{col 26}{space 2} .0456092{col 54}{space 4}-.8457092{col 67}{space 3}-.6669244
{txt}{space 7}/cut5 {c |}{col 14}{res}{space 2}-.7106027{col 26}{space 2} .0452469{col 54}{space 4} -.799285{col 67}{space 3}-.6219205
{txt}{space 7}/cut6 {c |}{col 14}{res}{space 2} .3065798{col 26}{space 2} .0432917{col 54}{space 4} .2217296{col 67}{space 3} .3914299
{txt}{space 7}/cut7 {c |}{col 14}{res}{space 2} .3656998{col 26}{space 2} .0433536{col 54}{space 4} .2807284{col 67}{space 3} .4506713
{txt}{space 7}/cut8 {c |}{col 14}{res}{space 2} .4706916{col 26}{space 2} .0435461{col 54}{space 4} .3853427{col 67}{space 3} .5560404
{txt}{space 7}/cut9 {c |}{col 14}{res}{space 2}  .617033{col 26}{space 2} .0442759{col 54}{space 4} .5302537{col 67}{space 3} .7038122
{txt}{space 6}/cut10 {c |}{col 14}{res}{space 2} .6617286{col 26}{space 2} .0444405{col 54}{space 4} .5746268{col 67}{space 3} .7488305
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. oprobit feelmorepain ib3.humanimaltxt, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res: -3339.689}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-3339.1814}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-3339.1814}  
{res}
{txt}{col 1}Ordered probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,938}
{txt}{col 57}{lalign 13:Wald chi2({res:2})}{col 70} = {res}{ralign 6:1.01}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.6035}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-3339.1814}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0002}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}feelmorepain{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimaltxt {c |}
Treatment 1  {c |}{col 14}{res}{space 2} .0125371{col 26}{space 2} .0591415{col 37}{space 1}    0.21{col 46}{space 3}0.832{col 54}{space 4} -.103378{col 67}{space 3} .1284523
{txt}Treatment 2  {c |}{col 14}{res}{space 2} .0572686{col 26}{space 2} .0589158{col 37}{space 1}    0.97{col 46}{space 3}0.331{col 54}{space 4}-.0582043{col 67}{space 3} .1727415
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-.9047836{col 26}{space 2} .0457891{col 54}{space 4}-.9945286{col 67}{space 3}-.8150385
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-.8279933{col 26}{space 2}  .045127{col 54}{space 4}-.9164406{col 67}{space 3} -.739546
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2}-.7682445{col 26}{space 2} .0444162{col 54}{space 4}-.8552987{col 67}{space 3}-.6811902
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2}-.7095123{col 26}{space 2} .0440793{col 54}{space 4}-.7959062{col 67}{space 3}-.6231185
{txt}{space 7}/cut5 {c |}{col 14}{res}{space 2}-.6728227{col 26}{space 2} .0439315{col 54}{space 4} -.758927{col 67}{space 3}-.5867185
{txt}{space 7}/cut6 {c |}{col 14}{res}{space 2} .2865998{col 26}{space 2} .0425694{col 54}{space 4} .2031652{col 67}{space 3} .3700343
{txt}{space 7}/cut7 {c |}{col 14}{res}{space 2} .3433421{col 26}{space 2} .0426988{col 54}{space 4}  .259654{col 67}{space 3} .4270303
{txt}{space 7}/cut8 {c |}{col 14}{res}{space 2} .4347452{col 26}{space 2} .0429309{col 54}{space 4} .3506022{col 67}{space 3} .5188882
{txt}{space 7}/cut9 {c |}{col 14}{res}{space 2} .5789248{col 26}{space 2} .0435752{col 54}{space 4} .4935189{col 67}{space 3} .6643306
{txt}{space 6}/cut10 {c |}{col 14}{res}{space 2} .6341768{col 26}{space 2} .0439526{col 54}{space 4} .5480313{col 67}{space 3} .7203224
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. oprobit protectmore ib3.humanimaltxt, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res: -3309.353}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-3306.0283}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-3306.0283}  
{res}
{txt}{col 1}Ordered probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,959}
{txt}{col 57}{lalign 13:Wald chi2({res:2})}{col 70} = {res}{ralign 6:6.64}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0362}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-3306.0283}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0010}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} protectmore{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimaltxt {c |}
Treatment 1  {c |}{col 14}{res}{space 2} .0514762{col 26}{space 2} .0594623{col 37}{space 1}    0.87{col 46}{space 3}0.387{col 54}{space 4}-.0650677{col 67}{space 3} .1680202
{txt}Treatment 2  {c |}{col 14}{res}{space 2} .1507367{col 26}{space 2} .0590647{col 37}{space 1}    2.55{col 46}{space 3}0.011{col 54}{space 4} .0349719{col 67}{space 3} .2665014
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-1.352116{col 26}{space 2} .0518868{col 54}{space 4}-1.453812{col 67}{space 3} -1.25042
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-1.276095{col 26}{space 2} .0504381{col 54}{space 4}-1.374952{col 67}{space 3}-1.177238
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2}-1.215813{col 26}{space 2} .0491938{col 54}{space 4}-1.312231{col 67}{space 3}-1.119395
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} -1.10238{col 26}{space 2}  .048099{col 54}{space 4}-1.196652{col 67}{space 3}-1.008108
{txt}{space 7}/cut5 {c |}{col 14}{res}{space 2}-1.050862{col 26}{space 2} .0473952{col 54}{space 4}-1.143755{col 67}{space 3}-.9579693
{txt}{space 7}/cut6 {c |}{col 14}{res}{space 2}-.0062505{col 26}{space 2} .0428007{col 54}{space 4}-.0901383{col 67}{space 3} .0776372
{txt}{space 7}/cut7 {c |}{col 14}{res}{space 2} .0412548{col 26}{space 2} .0428145{col 54}{space 4}-.0426601{col 67}{space 3} .1251697
{txt}{space 7}/cut8 {c |}{col 14}{res}{space 2} .1761371{col 26}{space 2}  .042955{col 54}{space 4} .0919468{col 67}{space 3} .2603274
{txt}{space 7}/cut9 {c |}{col 14}{res}{space 2} .3756819{col 26}{space 2} .0434164{col 54}{space 4} .2905874{col 67}{space 3} .4607765
{txt}{space 6}/cut10 {c |}{col 14}{res}{space 2} .4450759{col 26}{space 2} .0437019{col 54}{space 4} .3594218{col 67}{space 3}   .53073
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Experiment 1 – Extended Controls (Ordered Probit Regression)
. 
. oprobit revempmediatorcheck  ib3.humanimaltxt i.ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4 , robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-2914.9921}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-2859.5794}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-2859.5065}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-2859.5065}  
{res}
{txt}{col 1}Ordered probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,909}
{txt}{col 57}{lalign 13:Wald chi2({res:30})}{col 70} = {res}{ralign 6:106.13}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-2859.5065}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0190}

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                    revempmediatorcheck{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      z{col 73}   P>|z|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}humanimaltxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2} .1368966{col 53}{space 2} .0619478{col 64}{space 1}    2.21{col 73}{space 3}0.027{col 81}{space 4} .0154811{col 94}{space 3}  .258312
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2} .0175186{col 53}{space 2} .0597607{col 64}{space 1}    0.29{col 73}{space 3}0.769{col 81}{space 4}-.0996103{col 94}{space 3} .1346474
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}-.3393427{col 53}{space 2} .0585485{col 64}{space 1}   -5.80{col 73}{space 3}0.000{col 81}{space 4}-.4540956{col 94}{space 3}-.2245898
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2}-.2335211{col 53}{space 2} .2337617{col 64}{space 1}   -1.00{col 73}{space 3}0.318{col 81}{space 4}-.6916856{col 94}{space 3} .2246433
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2} -.285431{col 53}{space 2} .0851794{col 64}{space 1}   -3.35{col 73}{space 3}0.001{col 81}{space 4}-.4523795{col 94}{space 3}-.1184825
{txt}{space 39} {c |}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2}-.0603099{col 53}{space 2} .0677289{col 64}{space 1}   -0.89{col 73}{space 3}0.373{col 81}{space 4}-.1930561{col 94}{space 3} .0724364
{txt}{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2}-.0961961{col 53}{space 2} .1004103{col 64}{space 1}   -0.96{col 73}{space 3}0.338{col 81}{space 4}-.2929968{col 94}{space 3} .1006045
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2}-.0113844{col 53}{space 2}  .051261{col 64}{space 1}   -0.22{col 73}{space 3}0.824{col 81}{space 4} -.111854{col 94}{space 3} .0890853
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2} .0141181{col 53}{space 2} .0751529{col 64}{space 1}    0.19{col 73}{space 3}0.851{col 81}{space 4}-.1331789{col 94}{space 3} .1614151
{txt}{space 30}displaced {c |}{col 41}{res}{space 2} .0409301{col 53}{space 2} .0773107{col 64}{space 1}    0.53{col 73}{space 3}0.597{col 81}{space 4}-.1105962{col 94}{space 3} .1924563
{txt}{space 30}ukrainian {c |}{col 41}{res}{space 2}   .09995{col 53}{space 2} .1436873{col 64}{space 1}    0.70{col 73}{space 3}0.487{col 81}{space 4}-.1816719{col 94}{space 3}  .381572
{txt}{space 32}russian {c |}{col 41}{res}{space 2} .0157867{col 53}{space 2} .2056219{col 64}{space 1}    0.08{col 73}{space 3}0.939{col 81}{space 4}-.3872248{col 94}{space 3} .4187983
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2} .1300191{col 53}{space 2} .0897338{col 64}{space 1}    1.45{col 73}{space 3}0.147{col 81}{space 4}-.0458558{col 94}{space 3}  .305894
{txt}{space 33}female {c |}{col 41}{res}{space 2}-.2737269{col 53}{space 2} .0550685{col 64}{space 1}   -4.97{col 73}{space 3}0.000{col 81}{space 4}-.3816591{col 94}{space 3}-.1657947
{txt}{space 36}age {c |}{col 41}{res}{space 2} .0033496{col 53}{space 2} .0023853{col 64}{space 1}    1.40{col 73}{space 3}0.160{col 81}{space 4}-.0013255{col 94}{space 3} .0080247
{txt}{space 30}education {c |}{col 41}{res}{space 2} .0551603{col 53}{space 2} .0210347{col 64}{space 1}    2.62{col 73}{space 3}0.009{col 81}{space 4} .0139331{col 94}{space 3} .0963875
{txt}{space 33}income {c |}{col 41}{res}{space 2} .0853892{col 53}{space 2} .0329639{col 64}{space 1}    2.59{col 73}{space 3}0.010{col 81}{space 4} .0207811{col 94}{space 3} .1499973
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2}-.1143793{col 53}{space 2} .1161732{col 64}{space 1}   -0.98{col 73}{space 3}0.325{col 81}{space 4}-.3420745{col 94}{space 3}  .113316
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2} .0357547{col 53}{space 2} .0960942{col 64}{space 1}    0.37{col 73}{space 3}0.710{col 81}{space 4}-.1525865{col 94}{space 3} .2240958
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2} .1003777{col 53}{space 2} .1332957{col 64}{space 1}    0.75{col 73}{space 3}0.451{col 81}{space 4}-.1608771{col 94}{space 3} .3616326
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2} -.000125{col 53}{space 2} .1309246{col 64}{space 1}   -0.00{col 73}{space 3}0.999{col 81}{space 4}-.2567326{col 94}{space 3} .2564826
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2} .0139669{col 53}{space 2}   .20496{col 64}{space 1}    0.07{col 73}{space 3}0.946{col 81}{space 4}-.3877474{col 94}{space 3} .4156811
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2} .0152286{col 53}{space 2} .1204066{col 64}{space 1}    0.13{col 73}{space 3}0.899{col 81}{space 4} -.220764{col 94}{space 3} .2512211
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2} -.057677{col 53}{space 2}  .100126{col 64}{space 1}   -0.58{col 73}{space 3}0.565{col 81}{space 4}-.2539204{col 94}{space 3} .1385663
{txt}{space 31}Student  {c |}{col 41}{res}{space 2} .1958662{col 53}{space 2} .2197496{col 64}{space 1}    0.89{col 73}{space 3}0.373{col 81}{space 4}-.2348352{col 94}{space 3} .6265676
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2} .0380128{col 53}{space 2} .1684747{col 64}{space 1}    0.23{col 73}{space 3}0.821{col 81}{space 4}-.2921916{col 94}{space 3} .3682173
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2} .1193186{col 53}{space 2} .0732437{col 64}{space 1}    1.63{col 73}{space 3}0.103{col 81}{space 4}-.0242364{col 94}{space 3} .2628736
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2} .1265966{col 53}{space 2} .0980995{col 64}{space 1}    1.29{col 73}{space 3}0.197{col 81}{space 4}-.0656749{col 94}{space 3} .3188681
{txt}{space 31}Central  {c |}{col 41}{res}{space 2} .0805962{col 53}{space 2}   .08335{col 64}{space 1}    0.97{col 73}{space 3}0.334{col 81}{space 4}-.0827668{col 94}{space 3} .2439592
{txt}{space 33}South  {c |}{col 41}{res}{space 2} .0325881{col 53}{space 2} .0878157{col 64}{space 1}    0.37{col 73}{space 3}0.711{col 81}{space 4}-.1395276{col 94}{space 3} .2047037
{txt}{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 34}/cut1 {c |}{col 41}{res}{space 2}   -.8913{col 53}{space 2} .3187242{col 81}{space 4}-1.515988{col 94}{space 3} -.266612
{txt}{space 34}/cut2 {c |}{col 41}{res}{space 2}-.8230855{col 53}{space 2} .3180639{col 81}{space 4}-1.446479{col 94}{space 3}-.1996918
{txt}{space 34}/cut3 {c |}{col 41}{res}{space 2}-.6006221{col 53}{space 2} .3184171{col 81}{space 4}-1.224708{col 94}{space 3} .0234639
{txt}{space 34}/cut4 {c |}{col 41}{res}{space 2}-.4491006{col 53}{space 2} .3193775{col 81}{space 4}-1.075069{col 94}{space 3} .1768679
{txt}{space 34}/cut5 {c |}{col 41}{res}{space 2}-.3502763{col 53}{space 2}   .31966{col 81}{space 4}-.9767985{col 94}{space 3} .2762459
{txt}{space 34}/cut6 {c |}{col 41}{res}{space 2} 1.435645{col 53}{space 2} .3210227{col 81}{space 4} .8064516{col 94}{space 3} 2.064838
{txt}{space 34}/cut7 {c |}{col 41}{res}{space 2} 1.603223{col 53}{space 2} .3213515{col 81}{space 4} .9733856{col 94}{space 3}  2.23306
{txt}{space 34}/cut8 {c |}{col 41}{res}{space 2} 1.873566{col 53}{space 2} .3225618{col 81}{space 4} 1.241357{col 94}{space 3} 2.505776
{txt}{space 34}/cut9 {c |}{col 41}{res}{space 2}  2.05936{col 53}{space 2} .3230328{col 81}{space 4} 1.426227{col 94}{space 3} 2.692492
{txt}{space 33}/cut10 {c |}{col 41}{res}{space 2} 2.174772{col 53}{space 2} .3238951{col 81}{space 4} 1.539949{col 94}{space 3} 2.809595
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. oprobit protectmoral ib3.humanimaltxt i.ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4 , robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-3213.3697}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-3151.0646}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-3151.0493}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-3151.0493}  
{res}
{txt}{col 1}Ordered probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,854}
{txt}{col 57}{lalign 13:Wald chi2({res:30})}{col 70} = {res}{ralign 6:125.37}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-3151.0493}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0194}

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                           protectmoral{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      z{col 73}   P>|z|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}humanimaltxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2} .0429413{col 53}{space 2} .0605453{col 64}{space 1}    0.71{col 73}{space 3}0.478{col 81}{space 4}-.0757253{col 94}{space 3} .1616079
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2}-.0359429{col 53}{space 2}  .061633{col 64}{space 1}   -0.58{col 73}{space 3}0.560{col 81}{space 4}-.1567413{col 94}{space 3} .0848556
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}-.2094503{col 53}{space 2} .0574429{col 64}{space 1}   -3.65{col 73}{space 3}0.000{col 81}{space 4}-.3220363{col 94}{space 3}-.0968644
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2} .1512993{col 53}{space 2} .1987438{col 64}{space 1}    0.76{col 73}{space 3}0.446{col 81}{space 4}-.2382314{col 94}{space 3} .5408299
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2}-.0561361{col 53}{space 2} .0813877{col 64}{space 1}   -0.69{col 73}{space 3}0.490{col 81}{space 4}-.2156532{col 94}{space 3} .1033809
{txt}{space 39} {c |}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2} .0915918{col 53}{space 2} .0675685{col 64}{space 1}    1.36{col 73}{space 3}0.175{col 81}{space 4}  -.04084{col 94}{space 3} .2240236
{txt}{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2}-.1082857{col 53}{space 2} .0926524{col 64}{space 1}   -1.17{col 73}{space 3}0.243{col 81}{space 4}-.2898809{col 94}{space 3} .0733096
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2}-.0886152{col 53}{space 2} .0508862{col 64}{space 1}   -1.74{col 73}{space 3}0.082{col 81}{space 4}-.1883502{col 94}{space 3} .0111198
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2} .0636725{col 53}{space 2} .0736069{col 64}{space 1}    0.87{col 73}{space 3}0.387{col 81}{space 4}-.0805943{col 94}{space 3} .2079393
{txt}{space 30}displaced {c |}{col 41}{res}{space 2} .0870848{col 53}{space 2} .0766769{col 64}{space 1}    1.14{col 73}{space 3}0.256{col 81}{space 4}-.0631991{col 94}{space 3} .2373687
{txt}{space 30}ukrainian {c |}{col 41}{res}{space 2}-.0756849{col 53}{space 2} .1485818{col 64}{space 1}   -0.51{col 73}{space 3}0.610{col 81}{space 4}-.3668999{col 94}{space 3} .2155301
{txt}{space 32}russian {c |}{col 41}{res}{space 2} -.179755{col 53}{space 2} .2279267{col 64}{space 1}   -0.79{col 73}{space 3}0.430{col 81}{space 4}-.6264831{col 94}{space 3}  .266973
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2}-.0310672{col 53}{space 2} .0872527{col 64}{space 1}   -0.36{col 73}{space 3}0.722{col 81}{space 4}-.2020794{col 94}{space 3}  .139945
{txt}{space 33}female {c |}{col 41}{res}{space 2}  -.27484{col 53}{space 2} .0528508{col 64}{space 1}   -5.20{col 73}{space 3}0.000{col 81}{space 4}-.3784257{col 94}{space 3}-.1712544
{txt}{space 36}age {c |}{col 41}{res}{space 2} .0102988{col 53}{space 2} .0023187{col 64}{space 1}    4.44{col 73}{space 3}0.000{col 81}{space 4} .0057543{col 94}{space 3} .0148434
{txt}{space 30}education {c |}{col 41}{res}{space 2}-.0257762{col 53}{space 2} .0210162{col 64}{space 1}   -1.23{col 73}{space 3}0.220{col 81}{space 4}-.0669672{col 94}{space 3} .0154148
{txt}{space 33}income {c |}{col 41}{res}{space 2} .0057875{col 53}{space 2} .0330566{col 64}{space 1}    0.18{col 73}{space 3}0.861{col 81}{space 4}-.0590022{col 94}{space 3} .0705771
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2} .1099684{col 53}{space 2} .1255283{col 64}{space 1}    0.88{col 73}{space 3}0.381{col 81}{space 4}-.1360626{col 94}{space 3} .3559995
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2}-.0735364{col 53}{space 2} .0953168{col 64}{space 1}   -0.77{col 73}{space 3}0.440{col 81}{space 4}-.2603539{col 94}{space 3} .1132811
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2} .1140122{col 53}{space 2} .1347164{col 64}{space 1}    0.85{col 73}{space 3}0.397{col 81}{space 4} -.150027{col 94}{space 3} .3780515
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2}-.0983844{col 53}{space 2} .1257413{col 64}{space 1}   -0.78{col 73}{space 3}0.434{col 81}{space 4}-.3448328{col 94}{space 3} .1480641
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2}  .023283{col 53}{space 2} .2083994{col 64}{space 1}    0.11{col 73}{space 3}0.911{col 81}{space 4}-.3851723{col 94}{space 3} .4317383
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2} .0450405{col 53}{space 2} .1275676{col 64}{space 1}    0.35{col 73}{space 3}0.724{col 81}{space 4}-.2049874{col 94}{space 3} .2950685
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2} .0667193{col 53}{space 2} .1013705{col 64}{space 1}    0.66{col 73}{space 3}0.510{col 81}{space 4}-.1319631{col 94}{space 3} .2654018
{txt}{space 31}Student  {c |}{col 41}{res}{space 2}-.0569125{col 53}{space 2}  .241808{col 64}{space 1}   -0.24{col 73}{space 3}0.814{col 81}{space 4}-.5308475{col 94}{space 3} .4170225
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2} .0906963{col 53}{space 2} .1518939{col 64}{space 1}    0.60{col 73}{space 3}0.550{col 81}{space 4}-.2070103{col 94}{space 3} .3884028
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2} .0821674{col 53}{space 2} .0706658{col 64}{space 1}    1.16{col 73}{space 3}0.245{col 81}{space 4} -.056335{col 94}{space 3} .2206698
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2} .1669727{col 53}{space 2} .0981469{col 64}{space 1}    1.70{col 73}{space 3}0.089{col 81}{space 4}-.0253917{col 94}{space 3} .3593371
{txt}{space 31}Central  {c |}{col 41}{res}{space 2} .0602685{col 53}{space 2} .0847557{col 64}{space 1}    0.71{col 73}{space 3}0.477{col 81}{space 4}-.1058497{col 94}{space 3} .2263868
{txt}{space 33}South  {c |}{col 41}{res}{space 2} .1247914{col 53}{space 2} .0885957{col 64}{space 1}    1.41{col 73}{space 3}0.159{col 81}{space 4} -.048853{col 94}{space 3} .2984359
{txt}{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 34}/cut1 {c |}{col 41}{res}{space 2}-.7106244{col 53}{space 2} .3127568{col 81}{space 4}-1.323616{col 94}{space 3}-.0976323
{txt}{space 34}/cut2 {c |}{col 41}{res}{space 2}-.6375181{col 53}{space 2} .3128896{col 81}{space 4} -1.25077{col 94}{space 3}-.0242658
{txt}{space 34}/cut3 {c |}{col 41}{res}{space 2}-.5725976{col 53}{space 2} .3123813{col 81}{space 4}-1.184854{col 94}{space 3} .0396586
{txt}{space 34}/cut4 {c |}{col 41}{res}{space 2} -.472332{col 53}{space 2} .3121283{col 81}{space 4}-1.084092{col 94}{space 3} .1394282
{txt}{space 34}/cut5 {c |}{col 41}{res}{space 2}-.4220162{col 53}{space 2} .3119319{col 81}{space 4}-1.033392{col 94}{space 3} .1893592
{txt}{space 34}/cut6 {c |}{col 41}{res}{space 2} .6473468{col 53}{space 2}  .312731{col 81}{space 4} .0344054{col 94}{space 3} 1.260288
{txt}{space 34}/cut7 {c |}{col 41}{res}{space 2} .7108172{col 53}{space 2} .3129926{col 81}{space 4}  .097363{col 94}{space 3} 1.324271
{txt}{space 34}/cut8 {c |}{col 41}{res}{space 2} .8212979{col 53}{space 2} .3131666{col 81}{space 4} .2075026{col 94}{space 3} 1.435093
{txt}{space 34}/cut9 {c |}{col 41}{res}{space 2} .9760131{col 53}{space 2} .3135353{col 81}{space 4} .3614953{col 94}{space 3} 1.590531
{txt}{space 33}/cut10 {c |}{col 41}{res}{space 2} 1.021422{col 53}{space 2} .3137492{col 81}{space 4} .4064851{col 94}{space 3} 1.636359
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. oprobit feelmorepain ib3.humanimaltxt i.ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4 , robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-3219.5482}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-3111.9865}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-3111.9172}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-3111.9172}  
{res}
{txt}{col 1}Ordered probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,863}
{txt}{col 57}{lalign 13:Wald chi2({res:30})}{col 70} = {res}{ralign 6:223.28}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-3111.9172}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0334}

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                           feelmorepain{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      z{col 73}   P>|z|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}humanimaltxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2} .0454471{col 53}{space 2} .0615744{col 64}{space 1}    0.74{col 73}{space 3}0.460{col 81}{space 4}-.0752365{col 94}{space 3} .1661307
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2} .0440495{col 53}{space 2}  .060128{col 64}{space 1}    0.73{col 73}{space 3}0.464{col 81}{space 4}-.0737992{col 94}{space 3} .1618983
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}-.2290948{col 53}{space 2} .0588672{col 64}{space 1}   -3.89{col 73}{space 3}0.000{col 81}{space 4}-.3444723{col 94}{space 3}-.1137173
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2} .2326804{col 53}{space 2} .1949596{col 64}{space 1}    1.19{col 73}{space 3}0.233{col 81}{space 4}-.1494334{col 94}{space 3} .6147941
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2} .0588949{col 53}{space 2} .0817284{col 64}{space 1}    0.72{col 73}{space 3}0.471{col 81}{space 4}-.1012898{col 94}{space 3} .2190795
{txt}{space 39} {c |}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2} .0891726{col 53}{space 2} .0665235{col 64}{space 1}    1.34{col 73}{space 3}0.180{col 81}{space 4} -.041211{col 94}{space 3} .2195562
{txt}{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2}-.1348897{col 53}{space 2} .0850469{col 64}{space 1}   -1.59{col 73}{space 3}0.113{col 81}{space 4}-.3015785{col 94}{space 3}  .031799
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2} -.119952{col 53}{space 2} .0511465{col 64}{space 1}   -2.35{col 73}{space 3}0.019{col 81}{space 4}-.2201974{col 94}{space 3}-.0197067
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2}-.0203116{col 53}{space 2}  .072758{col 64}{space 1}   -0.28{col 73}{space 3}0.780{col 81}{space 4}-.1629146{col 94}{space 3} .1222914
{txt}{space 30}displaced {c |}{col 41}{res}{space 2} .0226231{col 53}{space 2} .0813079{col 64}{space 1}    0.28{col 73}{space 3}0.781{col 81}{space 4}-.1367374{col 94}{space 3} .1819836
{txt}{space 30}ukrainian {c |}{col 41}{res}{space 2} .0683188{col 53}{space 2} .1409161{col 64}{space 1}    0.48{col 73}{space 3}0.628{col 81}{space 4}-.2078716{col 94}{space 3} .3445091
{txt}{space 32}russian {c |}{col 41}{res}{space 2} .0799958{col 53}{space 2} .2051214{col 64}{space 1}    0.39{col 73}{space 3}0.697{col 81}{space 4}-.3220347{col 94}{space 3} .4820263
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2}-.0931335{col 53}{space 2} .0857664{col 64}{space 1}   -1.09{col 73}{space 3}0.278{col 81}{space 4}-.2612326{col 94}{space 3} .0749657
{txt}{space 33}female {c |}{col 41}{res}{space 2}-.2069042{col 53}{space 2} .0549357{col 64}{space 1}   -3.77{col 73}{space 3}0.000{col 81}{space 4}-.3145762{col 94}{space 3}-.0992322
{txt}{space 36}age {c |}{col 41}{res}{space 2} .0144662{col 53}{space 2} .0023368{col 64}{space 1}    6.19{col 73}{space 3}0.000{col 81}{space 4} .0098862{col 94}{space 3} .0190463
{txt}{space 30}education {c |}{col 41}{res}{space 2}-.0396481{col 53}{space 2} .0202377{col 64}{space 1}   -1.96{col 73}{space 3}0.050{col 81}{space 4}-.0793131{col 94}{space 3}  .000017
{txt}{space 33}income {c |}{col 41}{res}{space 2}-.0465495{col 53}{space 2}  .032791{col 64}{space 1}   -1.42{col 73}{space 3}0.156{col 81}{space 4}-.1108187{col 94}{space 3} .0177198
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2} .0757508{col 53}{space 2} .1250832{col 64}{space 1}    0.61{col 73}{space 3}0.545{col 81}{space 4}-.1694077{col 94}{space 3} .3209094
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2} .0691263{col 53}{space 2} .0966658{col 64}{space 1}    0.72{col 73}{space 3}0.475{col 81}{space 4}-.1203351{col 94}{space 3} .2585877
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2} .2723154{col 53}{space 2} .1530931{col 64}{space 1}    1.78{col 73}{space 3}0.075{col 81}{space 4}-.0277416{col 94}{space 3} .5723724
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2} .0618864{col 53}{space 2} .1270435{col 64}{space 1}    0.49{col 73}{space 3}0.626{col 81}{space 4}-.1871143{col 94}{space 3} .3108871
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2} .2781098{col 53}{space 2} .2176222{col 64}{space 1}    1.28{col 73}{space 3}0.201{col 81}{space 4}-.1484218{col 94}{space 3} .7046414
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2} .1269359{col 53}{space 2} .1248154{col 64}{space 1}    1.02{col 73}{space 3}0.309{col 81}{space 4}-.1176977{col 94}{space 3} .3715695
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2} .1946056{col 53}{space 2} .1009544{col 64}{space 1}    1.93{col 73}{space 3}0.054{col 81}{space 4}-.0032615{col 94}{space 3} .3924726
{txt}{space 31}Student  {c |}{col 41}{res}{space 2} .2821012{col 53}{space 2} .2344381{col 64}{space 1}    1.20{col 73}{space 3}0.229{col 81}{space 4} -.177389{col 94}{space 3} .7415914
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2} .0910528{col 53}{space 2} .1575796{col 64}{space 1}    0.58{col 73}{space 3}0.563{col 81}{space 4}-.2177976{col 94}{space 3} .3999032
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2} .0667408{col 53}{space 2} .0729055{col 64}{space 1}    0.92{col 73}{space 3}0.360{col 81}{space 4}-.0761514{col 94}{space 3} .2096329
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2} .2729165{col 53}{space 2} .0990805{col 64}{space 1}    2.75{col 73}{space 3}0.006{col 81}{space 4} .0787224{col 94}{space 3} .4671106
{txt}{space 31}Central  {c |}{col 41}{res}{space 2} .0314912{col 53}{space 2} .0886479{col 64}{space 1}    0.36{col 73}{space 3}0.722{col 81}{space 4}-.1422556{col 94}{space 3}  .205238
{txt}{space 33}South  {c |}{col 41}{res}{space 2}-.0393335{col 53}{space 2} .0898041{col 64}{space 1}   -0.44{col 73}{space 3}0.661{col 81}{space 4}-.2153464{col 94}{space 3} .1366793
{txt}{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 34}/cut1 {c |}{col 41}{res}{space 2}-.5343516{col 53}{space 2}  .310045{col 81}{space 4}-1.142029{col 94}{space 3} .0733254
{txt}{space 34}/cut2 {c |}{col 41}{res}{space 2}-.4531383{col 53}{space 2} .3101829{col 81}{space 4}-1.061086{col 94}{space 3} .1548091
{txt}{space 34}/cut3 {c |}{col 41}{res}{space 2} -.386415{col 53}{space 2} .3101731{col 81}{space 4} -.994343{col 94}{space 3}  .221513
{txt}{space 34}/cut4 {c |}{col 41}{res}{space 2}-.3226239{col 53}{space 2} .3101589{col 81}{space 4}-.9305241{col 94}{space 3} .2852764
{txt}{space 34}/cut5 {c |}{col 41}{res}{space 2}-.2833924{col 53}{space 2}  .309864{col 81}{space 4}-.8907147{col 94}{space 3} .3239298
{txt}{space 34}/cut6 {c |}{col 41}{res}{space 2} .7360735{col 53}{space 2} .3111605{col 81}{space 4} .1262101{col 94}{space 3} 1.345937
{txt}{space 34}/cut7 {c |}{col 41}{res}{space 2} .7981682{col 53}{space 2}  .311253{col 81}{space 4} .1881235{col 94}{space 3} 1.408213
{txt}{space 34}/cut8 {c |}{col 41}{res}{space 2} .8946628{col 53}{space 2} .3115822{col 81}{space 4} .2839729{col 94}{space 3} 1.505353
{txt}{space 34}/cut9 {c |}{col 41}{res}{space 2} 1.051194{col 53}{space 2} .3121172{col 81}{space 4} .4394557{col 94}{space 3} 1.662932
{txt}{space 33}/cut10 {c |}{col 41}{res}{space 2} 1.111699{col 53}{space 2} .3124401{col 81}{space 4} .4993275{col 94}{space 3}  1.72407
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. oprobit protectmore ib3.humanimaltxt i.ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4 , robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res: -3187.664}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-3121.2063}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-3121.1375}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-3121.1375}  
{res}
{txt}{col 1}Ordered probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,883}
{txt}{col 57}{lalign 13:Wald chi2({res:30})}{col 70} = {res}{ralign 6:127.96}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-3121.1375}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0209}

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                            protectmore{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      z{col 73}   P>|z|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}humanimaltxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2} .0882035{col 53}{space 2} .0613029{col 64}{space 1}    1.44{col 73}{space 3}0.150{col 81}{space 4} -.031948{col 94}{space 3}  .208355
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2} .1374226{col 53}{space 2} .0608544{col 64}{space 1}    2.26{col 73}{space 3}0.024{col 81}{space 4} .0181502{col 94}{space 3} .2566951
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}-.2628194{col 53}{space 2} .0584424{col 64}{space 1}   -4.50{col 73}{space 3}0.000{col 81}{space 4}-.3773645{col 94}{space 3}-.1482744
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2} .5802345{col 53}{space 2} .1944963{col 64}{space 1}    2.98{col 73}{space 3}0.003{col 81}{space 4} .1990287{col 94}{space 3} .9614403
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2}-.1268439{col 53}{space 2} .0827773{col 64}{space 1}   -1.53{col 73}{space 3}0.125{col 81}{space 4}-.2890844{col 94}{space 3} .0353967
{txt}{space 39} {c |}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2}-.0486123{col 53}{space 2} .0639309{col 64}{space 1}   -0.76{col 73}{space 3}0.447{col 81}{space 4}-.1739146{col 94}{space 3}   .07669
{txt}{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2}-.1743421{col 53}{space 2} .0955407{col 64}{space 1}   -1.82{col 73}{space 3}0.068{col 81}{space 4}-.3615984{col 94}{space 3} .0129142
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2} -.005981{col 53}{space 2}  .051094{col 64}{space 1}   -0.12{col 73}{space 3}0.907{col 81}{space 4}-.1061235{col 94}{space 3} .0941614
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2} .0039255{col 53}{space 2} .0729018{col 64}{space 1}    0.05{col 73}{space 3}0.957{col 81}{space 4}-.1389595{col 94}{space 3} .1468104
{txt}{space 30}displaced {c |}{col 41}{res}{space 2} .0533986{col 53}{space 2} .0813808{col 64}{space 1}    0.66{col 73}{space 3}0.512{col 81}{space 4}-.1061048{col 94}{space 3}  .212902
{txt}{space 30}ukrainian {c |}{col 41}{res}{space 2}  .191216{col 53}{space 2} .1402923{col 64}{space 1}    1.36{col 73}{space 3}0.173{col 81}{space 4}-.0837518{col 94}{space 3} .4661838
{txt}{space 32}russian {c |}{col 41}{res}{space 2} .0132599{col 53}{space 2} .2086827{col 64}{space 1}    0.06{col 73}{space 3}0.949{col 81}{space 4}-.3957507{col 94}{space 3} .4222706
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2}-.0061917{col 53}{space 2}  .088614{col 64}{space 1}   -0.07{col 73}{space 3}0.944{col 81}{space 4}-.1798721{col 94}{space 3} .1674886
{txt}{space 33}female {c |}{col 41}{res}{space 2} -.379672{col 53}{space 2} .0555502{col 64}{space 1}   -6.83{col 73}{space 3}0.000{col 81}{space 4}-.4885485{col 94}{space 3}-.2707955
{txt}{space 36}age {c |}{col 41}{res}{space 2} .0082239{col 53}{space 2} .0023113{col 64}{space 1}    3.56{col 73}{space 3}0.000{col 81}{space 4} .0036939{col 94}{space 3}  .012754
{txt}{space 30}education {c |}{col 41}{res}{space 2} .0102877{col 53}{space 2} .0204295{col 64}{space 1}    0.50{col 73}{space 3}0.615{col 81}{space 4}-.0297533{col 94}{space 3} .0503287
{txt}{space 33}income {c |}{col 41}{res}{space 2}-.0469403{col 53}{space 2} .0326779{col 64}{space 1}   -1.44{col 73}{space 3}0.151{col 81}{space 4}-.1109879{col 94}{space 3} .0171072
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2} .1122859{col 53}{space 2} .1247984{col 64}{space 1}    0.90{col 73}{space 3}0.368{col 81}{space 4}-.1323145{col 94}{space 3} .3568863
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2} .0515525{col 53}{space 2}  .094396{col 64}{space 1}    0.55{col 73}{space 3}0.585{col 81}{space 4}-.1334601{col 94}{space 3} .2365652
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2} .1801193{col 53}{space 2} .1438508{col 64}{space 1}    1.25{col 73}{space 3}0.211{col 81}{space 4}-.1018231{col 94}{space 3} .4620616
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2} .0311976{col 53}{space 2} .1238017{col 64}{space 1}    0.25{col 73}{space 3}0.801{col 81}{space 4}-.2114494{col 94}{space 3} .2738445
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2}  .201203{col 53}{space 2} .2276311{col 64}{space 1}    0.88{col 73}{space 3}0.377{col 81}{space 4}-.2449457{col 94}{space 3} .6473516
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2} .0589897{col 53}{space 2} .1257871{col 64}{space 1}    0.47{col 73}{space 3}0.639{col 81}{space 4}-.1875484{col 94}{space 3} .3055278
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2}  .008846{col 53}{space 2} .0982284{col 64}{space 1}    0.09{col 73}{space 3}0.928{col 81}{space 4}-.1836781{col 94}{space 3} .2013701
{txt}{space 31}Student  {c |}{col 41}{res}{space 2} .0148677{col 53}{space 2} .2536753{col 64}{space 1}    0.06{col 73}{space 3}0.953{col 81}{space 4}-.4823268{col 94}{space 3} .5120621
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2} .1040528{col 53}{space 2} .1485748{col 64}{space 1}    0.70{col 73}{space 3}0.484{col 81}{space 4}-.1871485{col 94}{space 3} .3952541
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2} .0664573{col 53}{space 2} .0725565{col 64}{space 1}    0.92{col 73}{space 3}0.360{col 81}{space 4}-.0757508{col 94}{space 3} .2086654
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2} .0749062{col 53}{space 2} .0988436{col 64}{space 1}    0.76{col 73}{space 3}0.449{col 81}{space 4}-.1188237{col 94}{space 3} .2686361
{txt}{space 31}Central  {c |}{col 41}{res}{space 2}-.0788335{col 53}{space 2} .0849972{col 64}{space 1}   -0.93{col 73}{space 3}0.354{col 81}{space 4}-.2454248{col 94}{space 3} .0877579
{txt}{space 33}South  {c |}{col 41}{res}{space 2}-.0342266{col 53}{space 2} .0894282{col 64}{space 1}   -0.38{col 73}{space 3}0.702{col 81}{space 4}-.2095026{col 94}{space 3} .1410494
{txt}{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 34}/cut1 {c |}{col 41}{res}{space 2}-1.096323{col 53}{space 2} .3047821{col 81}{space 4}-1.693685{col 94}{space 3}-.4989609
{txt}{space 34}/cut2 {c |}{col 41}{res}{space 2}-1.013201{col 53}{space 2} .3050582{col 81}{space 4}-1.611104{col 94}{space 3}-.4152978
{txt}{space 34}/cut3 {c |}{col 41}{res}{space 2}-.9504205{col 53}{space 2} .3045863{col 81}{space 4}-1.547399{col 94}{space 3}-.3534422
{txt}{space 34}/cut4 {c |}{col 41}{res}{space 2}-.8313847{col 53}{space 2} .3041678{col 81}{space 4}-1.427543{col 94}{space 3}-.2352267
{txt}{space 34}/cut5 {c |}{col 41}{res}{space 2}-.7773425{col 53}{space 2} .3036068{col 81}{space 4}-1.372401{col 94}{space 3}-.1822841
{txt}{space 34}/cut6 {c |}{col 41}{res}{space 2} .3222301{col 53}{space 2} .3030095{col 81}{space 4}-.2716576{col 94}{space 3} .9161179
{txt}{space 34}/cut7 {c |}{col 41}{res}{space 2} .3717115{col 53}{space 2} .3031079{col 81}{space 4} -.222369{col 94}{space 3}  .965792
{txt}{space 34}/cut8 {c |}{col 41}{res}{space 2} .5142828{col 53}{space 2} .3031158{col 81}{space 4}-.0798134{col 94}{space 3} 1.108379
{txt}{space 34}/cut9 {c |}{col 41}{res}{space 2} .7256771{col 53}{space 2} .3034904{col 81}{space 4} .1308468{col 94}{space 3} 1.320507
{txt}{space 33}/cut10 {c |}{col 41}{res}{space 2} .8000735{col 53}{space 2} .3034496{col 81}{space 4} .2053232{col 94}{space 3} 1.394824
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Experiment 1 – Empathy, Social Norms, Agency, and Protection from Harm (OLS Regression)
. 
. reg protectmore ib3.humanimaltxt revempmediatorcheck feelmorepain protectmoral, robust

{txt}Linear regression                               Number of obs     = {res}     1,835
                                                {txt}F(5, 1829)        =  {res}   141.54
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2842
                                                {txt}Root MSE          =    {res} 2.6215

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}        protectmore{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}humanimaltxt {c |}
{space 7}Treatment 1  {c |}{col 21}{res}{space 2} .0716397{col 33}{space 2} .1525679{col 44}{space 1}    0.47{col 53}{space 3}0.639{col 61}{space 4}-.2275858{col 74}{space 3} .3708653
{txt}{space 7}Treatment 2  {c |}{col 21}{res}{space 2} .3704453{col 33}{space 2} .1448399{col 44}{space 1}    2.56{col 53}{space 3}0.011{col 61}{space 4} .0863762{col 74}{space 3} .6545143
{txt}{space 19} {c |}
revempmediatorcheck {c |}{col 21}{res}{space 2} .2302882{col 33}{space 2} .0295242{col 44}{space 1}    7.80{col 53}{space 3}0.000{col 61}{space 4} .1723835{col 74}{space 3} .2881928
{txt}{space 7}feelmorepain {c |}{col 21}{res}{space 2} .2293999{col 33}{space 2}  .021944{col 44}{space 1}   10.45{col 53}{space 3}0.000{col 61}{space 4} .1863619{col 74}{space 3}  .272438
{txt}{space 7}protectmoral {c |}{col 21}{res}{space 2} .2757152{col 33}{space 2} .0226058{col 44}{space 1}   12.20{col 53}{space 3}0.000{col 61}{space 4} .2313793{col 74}{space 3} .3200512
{txt}{space 14}_cons {c |}{col 21}{res}{space 2} 2.486406{col 33}{space 2}  .205948{col 44}{space 1}   12.07{col 53}{space 3}0.000{col 61}{space 4} 2.082488{col 74}{space 3} 2.890324
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Experiment 1 Power Calculations
. 
. power oneway, n1(632) n2(647) n3(729) power(0.80 0.90 0.95 0.99)
{res}
{txt}Performing iteration ...
{res}
{p 0 2 2}{txt}Estimated{txt} between-group variance{txt} for one-way ANOVA{p_end}{txt}F test for group effect
{txt}{txt}{bind:H0: delta = 0}  {txt}versus  {bind:Ha: delta != 0}

  {txt}{c TLC}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c TRC}
  {txt}{c |}{txt}{txt}{ralign 8:alpha}{txt}{txt}{ralign 8:power}{txt}{txt}{ralign 8:N}{txt}{txt}{ralign 8:N_avg}{txt}{txt}{ralign 8:N1}{txt}{txt}{ralign 8:N2}{txt}{txt}{ralign 8:N3}{txt}{txt}{ralign 8:delta}{txt}{txt}{ralign 8:N_g}{txt}{txt}{ralign 8:Var_m}{txt}{txt}{ralign 8:Var_e}{txt}{txt} {c |}
  {txt}{c LT}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c RT}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.8}{res}{ralign 8:2,008}{res}{ralign 8:669.3}{res}{ralign 8:632}{res}{ralign 8:647}{res}{ralign 8:729}{res}{ralign 8:.06932}{res}{ralign 8:3}{res}{ralign 8:.00481}{res}{ralign 8:1}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.9}{res}{ralign 8:2,008}{res}{ralign 8:669.3}{res}{ralign 8:632}{res}{ralign 8:647}{res}{ralign 8:729}{res}{ralign 8:.07944}{res}{ralign 8:3}{res}{ralign 8:.00631}{res}{ralign 8:1}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.95}{res}{ralign 8:2,008}{res}{ralign 8:669.3}{res}{ralign 8:632}{res}{ralign 8:647}{res}{ralign 8:729}{res}{ralign 8:.08776}{res}{ralign 8:3}{res}{ralign 8:.0077}{res}{ralign 8:1}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.99}{res}{ralign 8:2,008}{res}{ralign 8:669.3}{res}{ralign 8:632}{res}{ralign 8:647}{res}{ralign 8:729}{res}{ralign 8:.1033}{res}{ralign 8:3}{res}{ralign 8:.01067}{res}{ralign 8:1}{txt} {c |}
  {txt}{c BLC}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c BRC}

{com}. esize twosample revempmediatorcheck if humanimaltxt~=2, by(humanimaltxt)

{txt}Effect size based on mean comparison

                               Obs per group:
                                 Treatment 1 =        622
                               Control Group =        722
{res}{col 1}{text}{hline 20}{c TT}{hline 12}{hline 12}{hline 12}
{col 1}{text}        Effect size{col 21}{c |}   Estimate{col 34}    [95% conf. interval]
{res}{col 1}{text}{hline 20}{c +}{hline 12}{hline 12}{hline 12}
{col 1}{text}          Cohen's {it:d}{col 21}{c |}{result}{space 2} .1262879{col 34}{space 3}  .018936{col 46}{space 3} .2335928
{col 1}{text}         Hedges's {it:g}{col 21}{c |}{result}{space 2} .1262173{col 34}{space 3} .0189255{col 46}{space 3} .2334622
{col 1}{text}{hline 20}{c BT}{hline 12}{hline 12}{hline 12}
{res}{txt}
{com}. esize twosample protectmore if humanimaltxt~=1, by(humanimaltxt)

{txt}Effect size based on mean comparison

                               Obs per group:
                                 Treatment 2 =        637
                               Control Group =        713
{res}{col 1}{text}{hline 20}{c TT}{hline 12}{hline 12}{hline 12}
{col 1}{text}        Effect size{col 21}{c |}   Estimate{col 34}    [95% conf. interval]
{res}{col 1}{text}{hline 20}{c +}{hline 12}{hline 12}{hline 12}
{col 1}{text}          Cohen's {it:d}{col 21}{c |}{result}{space 2}  .136307{col 34}{space 3} .0293016{col 46}{space 3} .2432619
{col 1}{text}         Hedges's {it:g}{col 21}{c |}{result}{space 2} .1362311{col 34}{space 3} .0292853{col 46}{space 3} .2431266
{col 1}{text}{hline 20}{c BT}{hline 12}{hline 12}{hline 12}
{res}{txt}
{com}. 
. *Sensitivity Analysis – Experiment 1 and Correlates of Empathy
. 
. reg revempmediatorcheck ib3.humanimaltxt, robust

{txt}Linear regression                               Number of obs     = {res}     1,981
                                                {txt}F(2, 1978)        =  {res}     2.83
                                                {txt}Prob > F          = {res}    0.0592
                                                {txt}R-squared         = {res}    0.0029
                                                {txt}Root MSE          =    {res}  2.227

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revempmedi~k{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimaltxt {c |}
Treatment 1  {c |}{col 14}{res}{space 2} .2808695{col 26}{space 2} .1220683{col 37}{space 1}    2.30{col 46}{space 3}0.021{col 54}{space 4} .0414736{col 67}{space 3} .5202654
{txt}Treatment 2  {c |}{col 14}{res}{space 2} .0589415{col 26}{space 2}  .120005{col 37}{space 1}    0.49{col 46}{space 3}0.623{col 54}{space 4} -.176408{col 67}{space 3} .2942909
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 5.277008{col 26}{space 2} .0810668{col 37}{space 1}   65.09{col 46}{space 3}0.000{col 54}{space 4} 5.118023{col 67}{space 3} 5.435994
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg protectmore ib3.humanimaltxt, robust

{txt}Linear regression                               Number of obs     = {res}     1,959
                                                {txt}F(2, 1956)        =  {res}     3.16
                                                {txt}Prob > F          = {res}    0.0427
                                                {txt}R-squared         = {res}    0.0032
                                                {txt}Root MSE          =    {res} 3.0967

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} protectmore{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimaltxt {c |}
Treatment 1  {c |}{col 14}{res}{space 2}  .169284{col 26}{space 2} .1722984{col 37}{space 1}    0.98{col 46}{space 3}0.326{col 54}{space 4}-.1686238{col 67}{space 3} .5071919
{txt}Treatment 2  {c |}{col 14}{res}{space 2} .4204073{col 26}{space 2} .1679175{col 37}{space 1}    2.50{col 46}{space 3}0.012{col 54}{space 4} .0910912{col 67}{space 3} .7497234
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 6.510519{col 26}{space 2} .1168801{col 37}{space 1}   55.70{col 46}{space 3}0.000{col 54}{space 4} 6.281296{col 67}{space 3} 6.739742
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg protectmore  ownpets if ownpets<2, robust

{txt}Linear regression                               Number of obs     = {res}     1,591
                                                {txt}F(1, 1589)        =  {res}    27.71
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0168
                                                {txt}Root MSE          =    {res} 3.1067

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} protectmore{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}ownpets {c |}{col 14}{res}{space 2}-.8297053{col 26}{space 2} .1576299{col 37}{space 1}   -5.26{col 46}{space 3}0.000{col 54}{space 4} -1.13889{col 67}{space 3}-.5205208
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 7.117647{col 26}{space 2} .1200762{col 37}{space 1}   59.28{col 46}{space 3}0.000{col 54}{space 4} 6.882123{col 67}{space 3} 7.353171
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg protectmore female, robust

{txt}Linear regression                               Number of obs     = {res}     1,959
                                                {txt}F(1, 1957)        =  {res}    52.91
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0259
                                                {txt}Root MSE          =    {res} 3.0605

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} protectmore{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}female {c |}{col 14}{res}{space 2} -1.00517{col 26}{space 2} .1381872{col 37}{space 1}   -7.27{col 46}{space 3}0.000{col 54}{space 4}-1.276179{col 67}{space 3}-.7341604
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 7.264261{col 26}{space 2} .1003774{col 37}{space 1}   72.37{col 46}{space 3}0.000{col 54}{space 4} 7.067403{col 67}{space 3} 7.461119
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg protectmore age, robust

{txt}Linear regression                               Number of obs     = {res}     1,959
                                                {txt}F(1, 1957)        =  {res}    15.72
                                                {txt}Prob > F          = {res}    0.0001
                                                {txt}R-squared         = {res}    0.0082
                                                {txt}Root MSE          =    {res} 3.0882

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} protectmore{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}age {c |}{col 14}{res}{space 2} .0174187{col 26}{space 2} .0043933{col 37}{space 1}    3.96{col 46}{space 3}0.000{col 54}{space 4} .0088028{col 67}{space 3} .0260347
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.785626{col 26}{space 2} .2453595{col 37}{space 1}   23.58{col 46}{space 3}0.000{col 54}{space 4} 5.304433{col 67}{space 3} 6.266819
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. regsensitivity bounds revempmediatorcheck humanimaltxt ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income employment region4 if humanimaltxt~=2, dmp robust
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: DMP (2022){col 48}{txt}Number of obs{col 67}{res}=       1,295
{col 48}{txt}Beta(short){col 67}{res}=      -0.132
{txt}Treatment{col 18}{res}: humanimaltxt{col 48}{txt}Beta(medium){col 67}{res}=      -0.123
{txt}Outcome{col 18}{res}: revempmediatorcheck{col 48}{txt}R2(short){col 67}{res}=       0.004
{col 48}{txt}R2(medium){col 67}{res}=       0.047
{col 48}{txt}Var(Y){col 67}{res}=       4.925
{col 48}{txt}Var(X){col 67}{res}=       0.994
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.983

{txt}Hypothesis{col 18}{res}: Beta < 0         {col 48}{txt}Breakdown point{col 67}{res}=          48%
{txt}Other Params{col 18}{res}: cbar = 1, rybar = +inf

{txt}{hline 80}
 rxbar{col 35} Beta
{hline 80}
{res}{col 2}0.000{col 35}{txt}[{res}-0.1228{txt}, {res}-0.1228{txt} ]
{col 2}{res}0.102{col 35}{txt}[{res}-0.1457{txt}, {res}-0.0999{txt} ]
{col 2}{res}0.204{col 35}{txt}[{res}-0.1694{txt}, {res}-0.0762{txt} ]
{col 2}{res}0.305{col 35}{txt}[{res}-0.1947{txt}, {res}-0.0509{txt} ]
{col 2}{res}0.407{col 35}{txt}[{res}-0.2229{txt}, {res}-0.0228{txt} ]
{col 2}{res}0.509{col 35}{txt}[{res}-0.2556{txt}, {res} 0.0100{txt} ]
{col 2}{res}0.611{col 35}{txt}[{res}-0.2963{txt}, {res} 0.0507{txt} ]
{col 2}{res}0.713{col 35}{txt}[{res}-0.3518{txt}, {res} 0.1062{txt} ]
{col 2}{res}0.815{col 35}{txt}[{res}-0.4409{txt}, {res} 0.1953{txt} ]
{col 2}{res}0.916{col 35}{txt}[{res}-0.6509{txt}, {res} 0.4053{txt} ]
{col 2}{res}0.995{col 35}{txt}[   {res}-inf{txt},    {res}+inf{txt} ]
{hline 80}

{com}. 
. regsensitivity bounds revempmediatorcheck humanimaltxt ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income employment region4 if humanimaltxt~=2, oster robust
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: Oster (2019){col 48}{txt}Number of obs{col 67}{res}=       1,295
{col 48}{txt}Beta(short){col 67}{res}=      -0.132
{txt}Treatment{col 18}{res}: humanimaltxt{col 48}{txt}Beta(medium){col 67}{res}=      -0.123
{txt}Outcome{col 18}{res}: revempmediatorcheck{col 48}{txt}R2(short){col 67}{res}=       0.004
{col 48}{txt}R2(medium){col 67}{res}=       0.047
{col 48}{txt}Var(Y){col 67}{res}=       4.925
{col 48}{txt}Var(X){col 67}{res}=       0.994
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.983

{txt}Hypothesis{col 18}{res}: Beta != 0         {col 48}{txt}Breakdown point{col 67}{res}=        54.3%
{txt}Other Params{col 18}{res}: R-squared(long) = 1

{txt}{hline 80}
 Delta{col 35} Beta
{hline 80}
{res}{col 2}-0.990{col 35}{txt}{{res} -0.29{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}-0.800{col 35}{txt}{{res} -0.26{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}-0.600{col 35}{txt}{{res} -0.23{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}-0.400{col 35}{txt}{{res} -0.20{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}-0.200{col 35}{txt}{{res} -0.16{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}0.000{col 35}{txt}{{res} -0.12{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}0.200{col 35}{txt}{{res} -0.08{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}0.400{col 35}{txt}{{res} -0.04{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}0.600{col 35}{txt}{{res}  0.01{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}0.800{col 35}{txt}{{res}  0.07{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}0.990{col 35}{txt}{{res}-60.98{txt}, {res}-26.62{txt}, {res}  0.13{txt} }
{hline 80}

{com}. 
. regsensitivity bounds protectmore humanimaltxt ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income employment region4 if humanimaltxt~=1, dmp  robust
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: DMP (2022){col 48}{txt}Number of obs{col 67}{res}=       1,300
{col 48}{txt}Beta(short){col 67}{res}=      -0.397
{txt}Treatment{col 18}{res}: humanimaltxt{col 48}{txt}Beta(medium){col 67}{res}=      -0.373
{txt}Outcome{col 18}{res}: protectmore{col 48}{txt}R2(short){col 67}{res}=       0.004
{col 48}{txt}R2(medium){col 67}{res}=       0.058
{col 48}{txt}Var(Y){col 67}{res}=       9.497
{col 48}{txt}Var(X){col 67}{res}=       0.249
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.248

{txt}Hypothesis{col 18}{res}: Beta < 0         {col 48}{txt}Breakdown point{col 67}{res}=          68%
{txt}Other Params{col 18}{res}: cbar = 1, rybar = +inf

{txt}{hline 80}
 rxbar{col 35} Beta
{hline 80}
{res}{col 2}0.000{col 35}{txt}[{res}-0.3732{txt}, {res}-0.3732{txt} ]
{col 2}{res}0.103{col 35}{txt}[{res}-0.4147{txt}, {res}-0.3316{txt} ]
{col 2}{res}0.206{col 35}{txt}[{res}-0.4577{txt}, {res}-0.2886{txt} ]
{col 2}{res}0.309{col 35}{txt}[{res}-0.5036{txt}, {res}-0.2427{txt} ]
{col 2}{res}0.412{col 35}{txt}[{res}-0.5548{txt}, {res}-0.1915{txt} ]
{col 2}{res}0.515{col 35}{txt}[{res}-0.6146{txt}, {res}-0.1318{txt} ]
{col 2}{res}0.618{col 35}{txt}[{res}-0.6892{txt}, {res}-0.0571{txt} ]
{col 2}{res}0.721{col 35}{txt}[{res}-0.7919{txt}, {res} 0.0456{txt} ]
{col 2}{res}0.824{col 35}{txt}[{res}-0.9598{txt}, {res} 0.2135{txt} ]
{col 2}{res}0.927{col 35}{txt}[{res}-1.3793{txt}, {res} 0.6329{txt} ]
{col 2}{res}0.998{col 35}{txt}[   {res}-inf{txt},    {res}+inf{txt} ]
{hline 80}

{com}. 
. regsensitivity bounds protectmore humanimaltxt ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income employment region4 if humanimaltxt~=1, oster  robust
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: Oster (2019){col 48}{txt}Number of obs{col 67}{res}=       1,300
{col 48}{txt}Beta(short){col 67}{res}=      -0.397
{txt}Treatment{col 18}{res}: humanimaltxt{col 48}{txt}Beta(medium){col 67}{res}=      -0.373
{txt}Outcome{col 18}{res}: protectmore{col 48}{txt}R2(short){col 67}{res}=       0.004
{col 48}{txt}R2(medium){col 67}{res}=       0.058
{col 48}{txt}Var(Y){col 67}{res}=       9.497
{col 48}{txt}Var(X){col 67}{res}=       0.249
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.248

{txt}Hypothesis{col 18}{res}: Beta != 0         {col 48}{txt}Breakdown point{col 67}{res}=        84.3%
{txt}Other Params{col 18}{res}: R-squared(long) = 1

{txt}{hline 80}
 Delta{col 35} Beta
{hline 80}
{res}{col 2}-0.990{col 35}{txt}{{res}  -0.76{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.800{col 35}{txt}{{res}  -0.69{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.600{col 35}{txt}{{res}  -0.61{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.400{col 35}{txt}{{res}  -0.54{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.200{col 35}{txt}{{res}  -0.46{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.000{col 35}{txt}{{res}  -0.37{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.200{col 35}{txt}{{res}  -0.29{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.400{col 35}{txt}{{res}  -0.20{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.600{col 35}{txt}{{res}  -0.11{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.800{col 35}{txt}{{res}  -0.02{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.990{col 35}{txt}{{res}-445.33{txt}, {res} -97.26{txt}, {res}   0.07{txt} }
{hline 80}

{com}. 
. regsensitivity bounds protectmore ownpets humanimaltxt dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income employment region4 if ownpets<2, dmp robust
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: DMP (2022){col 48}{txt}Number of obs{col 67}{res}=       1,534
{col 48}{txt}Beta(short){col 67}{res}=      -0.830
{txt}Treatment{col 18}{res}: ownpets{col 48}{txt}Beta(medium){col 67}{res}=      -0.673
{txt}Outcome{col 18}{res}: protectmore{col 48}{txt}R2(short){col 67}{res}=       0.017
{col 48}{txt}R2(medium){col 67}{res}=       0.060
{col 48}{txt}Var(Y){col 67}{res}=       9.695
{col 48}{txt}Var(X){col 67}{res}=       0.240
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.231

{txt}Hypothesis{col 18}{res}: Beta < 0         {col 48}{txt}Breakdown point{col 67}{res}=        47.1%
{txt}Other Params{col 18}{res}: cbar = 1, rybar = +inf

{txt}{hline 80}
 rxbar{col 35} Beta
{hline 80}
{res}{col 2}0.000{col 35}{txt}[{res}-0.6735{txt}, {res}-0.6735{txt} ]
{col 2}{res}0.099{col 35}{txt}[{res}-0.7980{txt}, {res}-0.5490{txt} ]
{col 2}{res}0.198{col 35}{txt}[{res}-0.9263{txt}, {res}-0.4206{txt} ]
{col 2}{res}0.296{col 35}{txt}[{res}-1.0632{txt}, {res}-0.2838{txt} ]
{col 2}{res}0.395{col 35}{txt}[{res}-1.2147{txt}, {res}-0.1323{txt} ]
{col 2}{res}0.494{col 35}{txt}[{res}-1.3902{txt}, {res} 0.0432{txt} ]
{col 2}{res}0.593{col 35}{txt}[{res}-1.6061{txt}, {res} 0.2591{txt} ]
{col 2}{res}0.692{col 35}{txt}[{res}-1.8959{txt}, {res} 0.5490{txt} ]
{col 2}{res}0.790{col 35}{txt}[{res}-2.3466{txt}, {res} 0.9996{txt} ]
{col 2}{res}0.889{col 35}{txt}[{res}-3.3145{txt}, {res} 1.9675{txt} ]
{col 2}{res}0.981{col 35}{txt}[   {res}-inf{txt},    {res}+inf{txt} ]
{hline 80}

{com}. 
. regsensitivity bounds protectmore ownpets humanimaltxt dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income employment region4 if ownpets<2, oster robust
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: Oster (2019){col 48}{txt}Number of obs{col 67}{res}=       1,534
{col 48}{txt}Beta(short){col 67}{res}=      -0.830
{txt}Treatment{col 18}{res}: ownpets{col 48}{txt}Beta(medium){col 67}{res}=      -0.673
{txt}Outcome{col 18}{res}: protectmore{col 48}{txt}R2(short){col 67}{res}=       0.017
{col 48}{txt}R2(medium){col 67}{res}=       0.060
{col 48}{txt}Var(Y){col 67}{res}=       9.695
{col 48}{txt}Var(X){col 67}{res}=       0.240
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.231

{txt}Hypothesis{col 18}{res}: Beta != 0         {col 48}{txt}Breakdown point{col 67}{res}=        18.4%
{txt}Other Params{col 18}{res}: R-squared(long) = 1

{txt}{hline 80}
 Delta{col 35} Beta
{hline 80}
{res}{col 2}-0.990{col 35}{txt}{{res}  -3.05{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.800{col 35}{txt}{{res}  -2.78{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.600{col 35}{txt}{{res}  -2.40{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.400{col 35}{txt}{{res}  -1.92{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.200{col 35}{txt}{{res}  -1.34{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.000{col 35}{txt}{{res}  -0.67{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.200{col 35}{txt}{{res}   0.06{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.400{col 35}{txt}{{res}   0.87{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.600{col 35}{txt}{{res}   1.81{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.800{col 35}{txt}{{res} -19.60{txt}, {res} -10.00{txt}, {res}   3.00{txt} }
{col 2}{res}0.990{col 35}{txt}{{res}-412.64{txt}, {res}  -7.88{txt}, {res}   4.73{txt} }
{hline 80}

{com}. 
. regsensitivity bounds protectmore female ownpets humanimaltxt dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker age education income employment region4, dmp robust
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: DMP (2022){col 48}{txt}Number of obs{col 67}{res}=       1,883
{col 48}{txt}Beta(short){col 67}{res}=      -0.947
{txt}Treatment{col 18}{res}: female{col 48}{txt}Beta(medium){col 67}{res}=      -1.104
{txt}Outcome{col 18}{res}: protectmore{col 48}{txt}R2(short){col 67}{res}=       0.023
{col 48}{txt}R2(medium){col 67}{res}=       0.045
{col 48}{txt}Var(Y){col 67}{res}=       9.501
{col 48}{txt}Var(X){col 67}{res}=       0.246
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.230

{txt}Hypothesis{col 18}{res}: Beta < 0         {col 48}{txt}Breakdown point{col 67}{res}=        54.4%
{txt}Other Params{col 18}{res}: cbar = 1, rybar = +inf

{txt}{hline 80}
 rxbar{col 35} Beta
{hline 80}
{res}{col 2}0.000{col 35}{txt}[{res}-1.1045{txt}, {res}-1.1045{txt} ]
{col 2}{res}0.097{col 35}{txt}[{res}-1.2678{txt}, {res}-0.9411{txt} ]
{col 2}{res}0.194{col 35}{txt}[{res}-1.4362{txt}, {res}-0.7728{txt} ]
{col 2}{res}0.290{col 35}{txt}[{res}-1.6155{txt}, {res}-0.5934{txt} ]
{col 2}{res}0.387{col 35}{txt}[{res}-1.8138{txt}, {res}-0.3951{txt} ]
{col 2}{res}0.484{col 35}{txt}[{res}-2.0431{txt}, {res}-0.1658{txt} ]
{col 2}{res}0.581{col 35}{txt}[{res}-2.3242{txt}, {res} 0.1153{txt} ]
{col 2}{res}0.677{col 35}{txt}[{res}-2.6996{txt}, {res} 0.4907{txt} ]
{col 2}{res}0.774{col 35}{txt}[{res}-3.2772{txt}, {res} 1.0683{txt} ]
{col 2}{res}0.871{col 35}{txt}[{res}-4.4826{txt}, {res} 2.2737{txt} ]
{col 2}{res}0.966{col 35}{txt}[   {res}-inf{txt},    {res}+inf{txt} ]
{hline 80}

{com}. 
. regsensitivity bounds protectmore female ownpets humanimaltxt dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker age education income employment region4, oster robust
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: Oster (2019){col 48}{txt}Number of obs{col 67}{res}=       1,883
{col 48}{txt}Beta(short){col 67}{res}=      -0.947
{txt}Treatment{col 18}{res}: female{col 48}{txt}Beta(medium){col 67}{res}=      -1.104
{txt}Outcome{col 18}{res}: protectmore{col 48}{txt}R2(short){col 67}{res}=       0.023
{col 48}{txt}R2(medium){col 67}{res}=       0.045
{col 48}{txt}Var(Y){col 67}{res}=       9.501
{col 48}{txt}Var(X){col 67}{res}=       0.246
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.230

{txt}Hypothesis{col 18}{res}: Beta != 0         {col 48}{txt}Breakdown point{col 67}{res}=        19.4%
{txt}Other Params{col 18}{res}: R-squared(long) = 1

{txt}{hline 80}
 Delta{col 35} Beta
{hline 80}
{res}{col 2}-0.990{col 35}{txt}{{res}   0.91{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.800{col 35}{txt}{{res}   0.83{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.600{col 35}{txt}{{res}   0.71{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.400{col 35}{txt}{{res}   0.50{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.200{col 35}{txt}{{res}   0.02{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.000{col 35}{txt}{{res}  -1.10{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.200{col 35}{txt}{{res}  -2.55{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.400{col 35}{txt}{{res}  -3.98{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.600{col 35}{txt}{{res}  -5.62{txt}, {res}   2.14{txt}, {res}   8.43{txt} }
{col 2}{res}0.800{col 35}{txt}{{res}  -8.06{txt}, {res}   1.85{txt}, {res}  17.06{txt} }
{col 2}{res}0.990{col 35}{txt}{{res} -14.28{txt}, {res}   1.71{txt}, {res} 247.79{txt} }
{hline 80}

{com}. 
. *Experiment 1 Mediation Analysis
. 
. *Generating complier variables (already coded)
. 
. *gen agreenudge = 1 if humanlessimp>5 & humanlessimp~=.
. *replace agreenudge = 1 if humanequalimp>5 & humanequalimp~=. & agreenudge==.
. *replace agreenudge = 1 if humanmoreimp>5 & humanmoreimp~=. & agreenudge==.
. *replace agreenudge = 0 if agreenudge==.
. 
. *gen peoplelessagree = 1 if empmediatortxt==1 & agreenudge==1
. *replace peoplelessagree = 0 if empmediatortxt==1 & agreenudge==0
. 
. *gen peopleequalagree = 1 if empmediatortxt==2 & agreenudge==1
. *replace peopleequalagree = 0 if empmediatortxt==2 & agreenudge==0
. 
. *gen peoplemoreagree = 1 if empmediatortxt==3 & agreenudge==1
. *replace peoplemoreagree = 0 if empmediatortxt==3 & agreenudge==0
. 
. tab humanequalimp peopleequalagree

  {txt}suffering of people {c |} complier with 1st exp
 equally important to {c |}     people equal
              animals {c |}         0          1 {c |}     Total
{hline 22}{c +}{hline 22}{c +}{hline 10}
0 - Strongly disagree {c |}{res}        25          0 {txt}{c |}{res}        25 
{txt}                    1 {c |}{res}         9          0 {txt}{c |}{res}         9 
{txt}                    2 {c |}{res}         7          0 {txt}{c |}{res}         7 
{txt}                    3 {c |}{res}         7          0 {txt}{c |}{res}         7 
{txt}                    4 {c |}{res}         9          0 {txt}{c |}{res}         9 
{txt}5 - Neither agree nor {c |}{res}       124          0 {txt}{c |}{res}       124 
{txt}                    6 {c |}{res}         0         10 {txt}{c |}{res}        10 
{txt}                    7 {c |}{res}         0         21 {txt}{c |}{res}        21 
{txt}                    8 {c |}{res}         0         31 {txt}{c |}{res}        31 
{txt}                    9 {c |}{res}         0         24 {txt}{c |}{res}        24 
{txt}  10 - Strongly agree {c |}{res}         0        333 {txt}{c |}{res}       333 
{txt}{hline 22}{c +}{hline 22}{c +}{hline 10}
                Total {c |}{res}       181        419 {txt}{c |}{res}       600 
{txt}
{com}. tab humanlessimp peoplelessagree

  {txt}suffering of people {c |} complier with 1st exp
  less important than {c |}  people matter less
              animals {c |}         0          1 {c |}     Total
{hline 22}{c +}{hline 22}{c +}{hline 10}
0 - Strongly disagree {c |}{res}       324          0 {txt}{c |}{res}       324 
{txt}                    1 {c |}{res}        31          0 {txt}{c |}{res}        31 
{txt}                    2 {c |}{res}        34          0 {txt}{c |}{res}        34 
{txt}                    3 {c |}{res}        28          0 {txt}{c |}{res}        28 
{txt}                    4 {c |}{res}        13          0 {txt}{c |}{res}        13 
{txt}5 - Neither agree nor {c |}{res}       235          0 {txt}{c |}{res}       235 
{txt}                    6 {c |}{res}         0         10 {txt}{c |}{res}        10 
{txt}                    7 {c |}{res}         0         16 {txt}{c |}{res}        16 
{txt}                    8 {c |}{res}         0         26 {txt}{c |}{res}        26 
{txt}                    9 {c |}{res}         0          7 {txt}{c |}{res}         7 
{txt}  10 - Strongly agree {c |}{res}         0         86 {txt}{c |}{res}        86 
{txt}{hline 22}{c +}{hline 22}{c +}{hline 10}
                Total {c |}{res}       665        145 {txt}{c |}{res}       810 
{txt}
{com}. tab humanmoreimp peoplemoreagree

  {txt}suffering of people {c |} complier with 1st exp
  more important than {c |}  people matter more
              animals {c |}         0          1 {c |}     Total
{hline 22}{c +}{hline 22}{c +}{hline 10}
0 - Strongly disagree {c |}{res}        59          0 {txt}{c |}{res}        59 
{txt}                    1 {c |}{res}         5          0 {txt}{c |}{res}         5 
{txt}                    2 {c |}{res}         9          0 {txt}{c |}{res}         9 
{txt}                    3 {c |}{res}        13          0 {txt}{c |}{res}        13 
{txt}                    4 {c |}{res}         6          0 {txt}{c |}{res}         6 
{txt}5 - Neither agree nor {c |}{res}       190          0 {txt}{c |}{res}       190 
{txt}                    6 {c |}{res}         0         14 {txt}{c |}{res}        14 
{txt}                    7 {c |}{res}         0         20 {txt}{c |}{res}        20 
{txt}                    8 {c |}{res}         0         40 {txt}{c |}{res}        40 
{txt}                    9 {c |}{res}         0         14 {txt}{c |}{res}        14 
{txt}  10 - Strongly agree {c |}{res}         0        164 {txt}{c |}{res}       164 
{txt}{hline 22}{c +}{hline 22}{c +}{hline 10}
                Total {c |}{res}       282        252 {txt}{c |}{res}       534 
{txt}
{com}. 
. *In total in GROUP C there were 19% Compliers in the Zoocentric Treatment, 72% Compliers in the Biocentric Treatment, and 44% Compliers in the Anthropocentric treatment.
. 
. tab agreenudge if empmediatortxt==1 & q123order==3 

 {txt}agreenudge {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        237       80.61       80.61
{txt}          1 {c |}{res}         57       19.39      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        294      100.00
{txt}
{com}. tab agreenudge if empmediatortxt==2 & q123order==3 

 {txt}agreenudge {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         48       27.59       27.59
{txt}          1 {c |}{res}        126       72.41      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        174      100.00
{txt}
{com}. tab agreenudge if empmediatortxt==3 & q123order==3 

 {txt}agreenudge {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         97       56.07       56.07
{txt}          1 {c |}{res}         76       43.93      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        173      100.00
{txt}
{com}. 
. *Balance tests indicate successful randomization into 3 Groups A, B, and C. Group means are below and t-tests are provided on the following page. 
. 
. iebaltab ownpets dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income employment urban_rural region4 if  q123order==3, groupvar(empmediatortxt) savexlsx(balancem1)

{res}{phang}Balance table saved in Excel format to: {browse "balancem1.xlsx":balancem1.xlsx}{p_end}
{txt}
{com}. 
. *Randomized Manipulation of Mediator (Group C)
. 
. iebaltab ownpets dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income employment urban_rural region4, groupvar(q123order) savexlsx(balanceg1)

{res}{phang}Balance table saved in Excel format to: {browse "balanceg1.xlsx":balanceg1.xlsx}{p_end}
{txt}
{com}. 
. *Experimental Treatment Effects by Group (OLS Regression)
. 
. reg protectmore ib3.humanimaltxt if q123order==1, robust

{txt}Linear regression                               Number of obs     = {res}       675
                                                {txt}F(2, 672)         =  {res}     0.17
                                                {txt}Prob > F          = {res}    0.8410
                                                {txt}R-squared         = {res}    0.0005
                                                {txt}Root MSE          =    {res} 3.3134

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} protectmore{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimaltxt {c |}
Treatment 1  {c |}{col 14}{res}{space 2} .1431637{col 26}{space 2} .3136112{col 37}{space 1}    0.46{col 46}{space 3}0.648{col 54}{space 4} -.472612{col 67}{space 3} .7589395
{txt}Treatment 2  {c |}{col 14}{res}{space 2} .1661619{col 26}{space 2} .3061711{col 37}{space 1}    0.54{col 46}{space 3}0.588{col 54}{space 4}-.4350052{col 67}{space 3} .7673291
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 6.359184{col 26}{space 2} .2125762{col 37}{space 1}   29.91{col 46}{space 3}0.000{col 54}{space 4}  5.94179{col 67}{space 3} 6.776577
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg protectmore ib3.humanimaltxt if q123order==2, robust

{txt}Linear regression                               Number of obs     = {res}       657
                                                {txt}F(2, 654)         =  {res}     1.59
                                                {txt}Prob > F          = {res}    0.2056
                                                {txt}R-squared         = {res}    0.0047
                                                {txt}Root MSE          =    {res} 2.9239

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} protectmore{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimaltxt {c |}
Treatment 1  {c |}{col 14}{res}{space 2} .1946935{col 26}{space 2} .2802701{col 37}{space 1}    0.69{col 46}{space 3}0.488{col 54}{space 4}-.3556444{col 67}{space 3} .7450313
{txt}Treatment 2  {c |}{col 14}{res}{space 2} .4800385{col 26}{space 2} .2696537{col 37}{space 1}    1.78{col 46}{space 3}0.076{col 54}{space 4}-.0494531{col 67}{space 3}  1.00953
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}    6.544{col 26}{space 2} .1772221{col 37}{space 1}   36.93{col 46}{space 3}0.000{col 54}{space 4} 6.196007{col 67}{space 3} 6.891993
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg protectmore ib3.humanimaltxt if q123order==3, robust

{txt}Linear regression                               Number of obs     = {res}       627
                                                {txt}F(2, 624)         =  {res}     2.35
                                                {txt}Prob > F          = {res}    0.0961
                                                {txt}R-squared         = {res}    0.0074
                                                {txt}Root MSE          =    {res} 3.0246

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} protectmore{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimaltxt {c |}
Treatment 1  {c |}{col 14}{res}{space 2} .1699809{col 26}{space 2} .3017963{col 37}{space 1}    0.56{col 46}{space 3}0.573{col 54}{space 4}-.4226786{col 67}{space 3} .7626404
{txt}Treatment 2  {c |}{col 14}{res}{space 2} .6125151{col 26}{space 2} .2960802{col 37}{space 1}    2.07{col 46}{space 3}0.039{col 54}{space 4} .0310808{col 67}{space 3}  1.19395
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 6.642202{col 26}{space 2}  .219082{col 37}{space 1}   30.32{col 46}{space 3}0.000{col 54}{space 4} 6.211975{col 67}{space 3} 7.072429
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Manipulation of Human Empathy in GROUP C (OLS Regression)
. 
. reg revempmediatorcheck ib3.humanimaltxt ib2.empmediatortxt##agreenudge if q123order==3, robust

{txt}Linear regression                               Number of obs     = {res}       631
                                                {txt}F(7, 623)         =  {res}    10.37
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1010
                                                {txt}Root MSE          =    {res} 2.1547

{txt}{hline 33}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 34}{c |}{col 46}    Robust
{col 1}             revempmediatorcheck{col 34}{c |} Coefficient{col 46}  std. err.{col 58}      t{col 66}   P>|t|{col 74}     [95% con{col 87}f. interval]
{hline 33}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}humanimaltxt {c |}
{space 20}Treatment 1  {c |}{col 34}{res}{space 2}   .19631{col 46}{space 2} .2119713{col 57}{space 1}    0.93{col 66}{space 3}0.355{col 74}{space 4}-.2199547{col 87}{space 3} .6125748
{txt}{space 20}Treatment 2  {c |}{col 34}{res}{space 2} .2568502{col 46}{space 2} .2087265{col 57}{space 1}    1.23{col 66}{space 3}0.219{col 74}{space 4}-.1530426{col 87}{space 3}  .666743
{txt}{space 32} {c |}
{space 18}empmediatortxt {c |}
{space 2}(Variant 1) S1.1. people less  {c |}{col 34}{res}{space 2}-.4024123{col 46}{space 2} .3363018{col 57}{space 1}   -1.20{col 66}{space 3}0.232{col 74}{space 4}-1.062835{col 87}{space 3} .2580102
{txt}{space 2}(Variant 3) S1.3. people more  {c |}{col 34}{res}{space 2} -1.59543{col 46}{space 2} .3475683{col 57}{space 1}   -4.59{col 66}{space 3}0.000{col 74}{space 4}-2.277978{col 87}{space 3}-.9128831
{txt}{space 32} {c |}
{space 20}1.agreenudge {c |}{col 34}{res}{space 2}-1.566097{col 46}{space 2} .3562388{col 57}{space 1}   -4.40{col 66}{space 3}0.000{col 74}{space 4}-2.265672{col 87}{space 3}-.8665232
{txt}{space 32} {c |}
{space 7}empmediatortxt#agreenudge {c |}
(Variant 1) S1.1. people less#1  {c |}{col 34}{res}{space 2} .0064961{col 46}{space 2} .4968212{col 57}{space 1}    0.01{col 66}{space 3}0.990{col 74}{space 4}-.9691509{col 87}{space 3} .9821431
{txt}(Variant 3) S1.3. people more#1  {c |}{col 34}{res}{space 2} 3.162489{col 46}{space 2} .4999109{col 57}{space 1}    6.33{col 66}{space 3}0.000{col 74}{space 4} 2.180774{col 87}{space 3} 4.144204
{txt}{space 32} {c |}
{space 27}_cons {c |}{col 34}{res}{space 2} 6.304757{col 46}{space 2}  .329353{col 57}{space 1}   19.14{col 66}{space 3}0.000{col 74}{space 4} 5.657981{col 87}{space 3} 6.951534
{txt}{hline 33}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg protectmore ib3.humanimaltxt ib2.empmediatortxt##agreenudge if q123order==3, robust

{txt}Linear regression                               Number of obs     = {res}       627
                                                {txt}F(7, 619)         =  {res}    11.00
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0923
                                                {txt}Root MSE          =    {res} 2.9041

{txt}{hline 33}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 34}{c |}{col 46}    Robust
{col 1}                     protectmore{col 34}{c |} Coefficient{col 46}  std. err.{col 58}      t{col 66}   P>|t|{col 74}     [95% con{col 87}f. interval]
{hline 33}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}humanimaltxt {c |}
{space 20}Treatment 1  {c |}{col 34}{res}{space 2} .1174504{col 46}{space 2} .2892376{col 57}{space 1}    0.41{col 66}{space 3}0.685{col 74}{space 4}-.4505555{col 87}{space 3} .6854563
{txt}{space 20}Treatment 2  {c |}{col 34}{res}{space 2}  .673753{col 46}{space 2} .2856321{col 57}{space 1}    2.36{col 66}{space 3}0.019{col 74}{space 4} .1128275{col 87}{space 3} 1.234678
{txt}{space 32} {c |}
{space 18}empmediatortxt {c |}
{space 2}(Variant 1) S1.1. people less  {c |}{col 34}{res}{space 2} -.781067{col 46}{space 2} .4369047{col 57}{space 1}   -1.79{col 66}{space 3}0.074{col 74}{space 4}-1.639062{col 87}{space 3} .0769281
{txt}{space 2}(Variant 3) S1.3. people more  {c |}{col 34}{res}{space 2} -2.58488{col 46}{space 2} .4722167{col 57}{space 1}   -5.47{col 66}{space 3}0.000{col 74}{space 4}-3.512221{col 87}{space 3}-1.657539
{txt}{space 32} {c |}
{space 20}1.agreenudge {c |}{col 34}{res}{space 2}-1.724727{col 46}{space 2} .4894544{col 57}{space 1}   -3.52{col 66}{space 3}0.000{col 74}{space 4}-2.685919{col 87}{space 3}-.7635343
{txt}{space 32} {c |}
{space 7}empmediatortxt#agreenudge {c |}
(Variant 1) S1.1. people less#1  {c |}{col 34}{res}{space 2} 1.662781{col 46}{space 2} .6523659{col 57}{space 1}    2.55{col 66}{space 3}0.011{col 74}{space 4} .3816627{col 87}{space 3}   2.9439
{txt}(Variant 3) S1.3. people more#1  {c |}{col 34}{res}{space 2} 4.631971{col 46}{space 2} .6318701{col 57}{space 1}    7.33{col 66}{space 3}0.000{col 74}{space 4} 3.391102{col 87}{space 3}  5.87284
{txt}{space 32} {c |}
{space 27}_cons {c |}{col 34}{res}{space 2} 7.688204{col 46}{space 2} .4394788{col 57}{space 1}   17.49{col 66}{space 3}0.000{col 74}{space 4} 6.825154{col 87}{space 3} 8.551254
{txt}{hline 33}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Anthropocentric Empathy Manipulation Check (Figure)
. 
. reg revempmediatorcheck ib3.humanimaltxt ib2.empmediatortxt##agreenudge if q123order==3, robust

{txt}Linear regression                               Number of obs     = {res}       631
                                                {txt}F(7, 623)         =  {res}    10.37
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1010
                                                {txt}Root MSE          =    {res} 2.1547

{txt}{hline 33}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 34}{c |}{col 46}    Robust
{col 1}             revempmediatorcheck{col 34}{c |} Coefficient{col 46}  std. err.{col 58}      t{col 66}   P>|t|{col 74}     [95% con{col 87}f. interval]
{hline 33}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}humanimaltxt {c |}
{space 20}Treatment 1  {c |}{col 34}{res}{space 2}   .19631{col 46}{space 2} .2119713{col 57}{space 1}    0.93{col 66}{space 3}0.355{col 74}{space 4}-.2199547{col 87}{space 3} .6125748
{txt}{space 20}Treatment 2  {c |}{col 34}{res}{space 2} .2568502{col 46}{space 2} .2087265{col 57}{space 1}    1.23{col 66}{space 3}0.219{col 74}{space 4}-.1530426{col 87}{space 3}  .666743
{txt}{space 32} {c |}
{space 18}empmediatortxt {c |}
{space 2}(Variant 1) S1.1. people less  {c |}{col 34}{res}{space 2}-.4024123{col 46}{space 2} .3363018{col 57}{space 1}   -1.20{col 66}{space 3}0.232{col 74}{space 4}-1.062835{col 87}{space 3} .2580102
{txt}{space 2}(Variant 3) S1.3. people more  {c |}{col 34}{res}{space 2} -1.59543{col 46}{space 2} .3475683{col 57}{space 1}   -4.59{col 66}{space 3}0.000{col 74}{space 4}-2.277978{col 87}{space 3}-.9128831
{txt}{space 32} {c |}
{space 20}1.agreenudge {c |}{col 34}{res}{space 2}-1.566097{col 46}{space 2} .3562388{col 57}{space 1}   -4.40{col 66}{space 3}0.000{col 74}{space 4}-2.265672{col 87}{space 3}-.8665232
{txt}{space 32} {c |}
{space 7}empmediatortxt#agreenudge {c |}
(Variant 1) S1.1. people less#1  {c |}{col 34}{res}{space 2} .0064961{col 46}{space 2} .4968212{col 57}{space 1}    0.01{col 66}{space 3}0.990{col 74}{space 4}-.9691509{col 87}{space 3} .9821431
{txt}(Variant 3) S1.3. people more#1  {c |}{col 34}{res}{space 2} 3.162489{col 46}{space 2} .4999109{col 57}{space 1}    6.33{col 66}{space 3}0.000{col 74}{space 4} 2.180774{col 87}{space 3} 4.144204
{txt}{space 32} {c |}
{space 27}_cons {c |}{col 34}{res}{space 2} 6.304757{col 46}{space 2}  .329353{col 57}{space 1}   19.14{col 66}{space 3}0.000{col 74}{space 4} 5.657981{col 87}{space 3} 6.951534
{txt}{hline 33}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins ib2.empmediatortxt#agreenudge
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:631}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 34}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 35}{c |}{col 47} Delta-method
{col 35}{c |}     Margin{col 47}   std. err.{col 59}      t{col 67}   P>|t|{col 75}     [95% con{col 88}f. interval]
{hline 34}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}empmediatortxt#agreenudge {c |}
{space 1}(Variant 1) S1.1. people less#0  {c |}{col 35}{res}{space 2} 6.050359{col 47}{space 2} .1420217{col 58}{space 1}   42.60{col 67}{space 3}0.000{col 75}{space 4}  5.77146{col 88}{space 3} 6.329258
{txt}{space 1}(Variant 1) S1.1. people less#1  {c |}{col 35}{res}{space 2} 4.490758{col 47}{space 2} .3162237{col 58}{space 1}   14.20{col 67}{space 3}0.000{col 75}{space 4} 3.869764{col 88}{space 3} 5.111751
{txt}(Variant 2) S1.2. people equal#0  {c |}{col 35}{res}{space 2} 6.452771{col 47}{space 2} .3047684{col 58}{space 1}   21.17{col 67}{space 3}0.000{col 75}{space 4} 5.854274{col 88}{space 3} 7.051269
{txt}(Variant 2) S1.2. people equal#1  {c |}{col 35}{res}{space 2} 4.886674{col 47}{space 2} .1854284{col 58}{space 1}   26.35{col 67}{space 3}0.000{col 75}{space 4} 4.522534{col 88}{space 3} 5.250814
{txt}{space 1}(Variant 3) S1.3. people more#0  {c |}{col 35}{res}{space 2} 4.857341{col 47}{space 2} .1657814{col 58}{space 1}   29.30{col 67}{space 3}0.000{col 75}{space 4} 4.531783{col 88}{space 3} 5.182899
{txt}{space 1}(Variant 3) S1.3. people more#1  {c |}{col 35}{res}{space 2} 6.453732{col 47}{space 2} .3087714{col 58}{space 1}   20.90{col 67}{space 3}0.000{col 75}{space 4} 5.847374{col 88}{space 3} 7.060091
{txt}{hline 34}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:empmediatortxt agreenudge}{p_end}
{res}{txt}
{com}. 
. *Note additional formatting required
. 
. *Support for More Human Protects (Empathy Mediator Margins Figure)
. 
. reg protectmore ib3.humanimaltxt ib2.empmediatortxt##agreenudge if q123order==3, robust

{txt}Linear regression                               Number of obs     = {res}       627
                                                {txt}F(7, 619)         =  {res}    11.00
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0923
                                                {txt}Root MSE          =    {res} 2.9041

{txt}{hline 33}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 34}{c |}{col 46}    Robust
{col 1}                     protectmore{col 34}{c |} Coefficient{col 46}  std. err.{col 58}      t{col 66}   P>|t|{col 74}     [95% con{col 87}f. interval]
{hline 33}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}humanimaltxt {c |}
{space 20}Treatment 1  {c |}{col 34}{res}{space 2} .1174504{col 46}{space 2} .2892376{col 57}{space 1}    0.41{col 66}{space 3}0.685{col 74}{space 4}-.4505555{col 87}{space 3} .6854563
{txt}{space 20}Treatment 2  {c |}{col 34}{res}{space 2}  .673753{col 46}{space 2} .2856321{col 57}{space 1}    2.36{col 66}{space 3}0.019{col 74}{space 4} .1128275{col 87}{space 3} 1.234678
{txt}{space 32} {c |}
{space 18}empmediatortxt {c |}
{space 2}(Variant 1) S1.1. people less  {c |}{col 34}{res}{space 2} -.781067{col 46}{space 2} .4369047{col 57}{space 1}   -1.79{col 66}{space 3}0.074{col 74}{space 4}-1.639062{col 87}{space 3} .0769281
{txt}{space 2}(Variant 3) S1.3. people more  {c |}{col 34}{res}{space 2} -2.58488{col 46}{space 2} .4722167{col 57}{space 1}   -5.47{col 66}{space 3}0.000{col 74}{space 4}-3.512221{col 87}{space 3}-1.657539
{txt}{space 32} {c |}
{space 20}1.agreenudge {c |}{col 34}{res}{space 2}-1.724727{col 46}{space 2} .4894544{col 57}{space 1}   -3.52{col 66}{space 3}0.000{col 74}{space 4}-2.685919{col 87}{space 3}-.7635343
{txt}{space 32} {c |}
{space 7}empmediatortxt#agreenudge {c |}
(Variant 1) S1.1. people less#1  {c |}{col 34}{res}{space 2} 1.662781{col 46}{space 2} .6523659{col 57}{space 1}    2.55{col 66}{space 3}0.011{col 74}{space 4} .3816627{col 87}{space 3}   2.9439
{txt}(Variant 3) S1.3. people more#1  {c |}{col 34}{res}{space 2} 4.631971{col 46}{space 2} .6318701{col 57}{space 1}    7.33{col 66}{space 3}0.000{col 74}{space 4} 3.391102{col 87}{space 3}  5.87284
{txt}{space 32} {c |}
{space 27}_cons {c |}{col 34}{res}{space 2} 7.688204{col 46}{space 2} .4394788{col 57}{space 1}   17.49{col 66}{space 3}0.000{col 74}{space 4} 6.825154{col 87}{space 3} 8.551254
{txt}{hline 33}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins ib2.empmediatortxt#agreenudge
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:627}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 34}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 35}{c |}{col 47} Delta-method
{col 35}{c |}     Margin{col 47}   std. err.{col 59}      t{col 67}   P>|t|{col 75}     [95% con{col 88}f. interval]
{hline 34}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}empmediatortxt#agreenudge {c |}
{space 1}(Variant 1) S1.1. people less#0  {c |}{col 35}{res}{space 2} 7.171847{col 47}{space 2}  .194931{col 58}{space 1}   36.79{col 67}{space 3}0.000{col 75}{space 4} 6.789041{col 88}{space 3} 7.554654
{txt}{space 1}(Variant 1) S1.1. people less#1  {c |}{col 35}{res}{space 2} 7.109902{col 47}{space 2} .3840417{col 58}{space 1}   18.51{col 67}{space 3}0.000{col 75}{space 4} 6.355719{col 88}{space 3} 7.864085
{txt}(Variant 2) S1.2. people equal#0  {c |}{col 35}{res}{space 2} 7.952914{col 47}{space 2} .3907715{col 58}{space 1}   20.35{col 67}{space 3}0.000{col 75}{space 4} 7.185516{col 88}{space 3} 8.720313
{txt}(Variant 2) S1.2. people equal#1  {c |}{col 35}{res}{space 2} 6.228188{col 47}{space 2} .2964252{col 58}{space 1}   21.01{col 67}{space 3}0.000{col 75}{space 4} 5.646067{col 88}{space 3} 6.810309
{txt}{space 1}(Variant 3) S1.3. people more#0  {c |}{col 35}{res}{space 2} 5.368034{col 47}{space 2} .2636727{col 58}{space 1}   20.36{col 67}{space 3}0.000{col 75}{space 4} 4.850233{col 88}{space 3} 5.885836
{txt}{space 1}(Variant 3) S1.3. people more#1  {c |}{col 35}{res}{space 2} 8.275279{col 47}{space 2} .3007072{col 58}{space 1}   27.52{col 67}{space 3}0.000{col 75}{space 4} 7.684749{col 88}{space 3} 8.865809
{txt}{hline 34}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot 
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:empmediatortxt agreenudge}{p_end}
{res}{txt}
{com}. 
. *Note additional formatting required
. 
. *Support for More Human Protects (Empathy Mediator Bar Figure)
. 
. cibar protectmore if q123order==3, over1(empmediatortxt) over2(agreenudge)
{res}{txt}
{com}. 
. *Empathy and Human vs Animal Suffering (OLS Regression)
. 
. reg revempmediatorcheck ib3.humanimaltxt if q123order==1, robust

{txt}Linear regression                               Number of obs     = {res}       689
                                                {txt}F(2, 686)         =  {res}     0.35
                                                {txt}Prob > F          = {res}    0.7032
                                                {txt}R-squared         = {res}    0.0010
                                                {txt}Root MSE          =    {res} 2.0456

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revempmedi~k{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimaltxt {c |}
Treatment 1  {c |}{col 14}{res}{space 2} .1092917{col 26}{space 2} .1873316{col 37}{space 1}    0.58{col 46}{space 3}0.560{col 54}{space 4}-.2585203{col 67}{space 3} .4771038
{txt}Treatment 2  {c |}{col 14}{res}{space 2}-.0497992{col 26}{space 2} .1910916{col 37}{space 1}   -0.26{col 46}{space 3}0.794{col 54}{space 4}-.4249939{col 67}{space 3} .3253955
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 5.449799{col 26}{space 2} .1293985{col 37}{space 1}   42.12{col 46}{space 3}0.000{col 54}{space 4} 5.195735{col 67}{space 3} 5.703864
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revempmediatorcheck ib3.humanimaltxt ib2.empmediatortxt##agreenudge if q123order==2, robust

{txt}Linear regression                               Number of obs     = {res}       661
                                                {txt}F(7, 653)         =  {res}     3.88
                                                {txt}Prob > F          = {res}    0.0004
                                                {txt}R-squared         = {res}    0.0414
                                                {txt}Root MSE          =    {res} 2.3203

{txt}{hline 33}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 34}{c |}{col 46}    Robust
{col 1}             revempmediatorcheck{col 34}{c |} Coefficient{col 46}  std. err.{col 58}      t{col 66}   P>|t|{col 74}     [95% con{col 87}f. interval]
{hline 33}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}humanimaltxt {c |}
{space 20}Treatment 1  {c |}{col 34}{res}{space 2} .5043044{col 46}{space 2} .2247991{col 57}{space 1}    2.24{col 66}{space 3}0.025{col 74}{space 4} .0628881{col 87}{space 3} .9457207
{txt}{space 20}Treatment 2  {c |}{col 34}{res}{space 2} .0021675{col 46}{space 2} .2101188{col 57}{space 1}    0.01{col 66}{space 3}0.992{col 74}{space 4}-.4104224{col 87}{space 3} .4147574
{txt}{space 32} {c |}
{space 18}empmediatortxt {c |}
{space 2}(Variant 1) S1.1. people less  {c |}{col 34}{res}{space 2}-1.035627{col 46}{space 2} .3024231{col 57}{space 1}   -3.42{col 66}{space 3}0.001{col 74}{space 4}-1.629466{col 87}{space 3}-.4417878
{txt}{space 2}(Variant 3) S1.3. people more  {c |}{col 34}{res}{space 2}-1.241612{col 46}{space 2} .3266894{col 57}{space 1}   -3.80{col 66}{space 3}0.000{col 74}{space 4}-1.883101{col 87}{space 3}-.6001237
{txt}{space 32} {c |}
{space 20}1.agreenudge {c |}{col 34}{res}{space 2}-1.034992{col 46}{space 2} .3564713{col 57}{space 1}   -2.90{col 66}{space 3}0.004{col 74}{space 4}-1.734961{col 87}{space 3}-.3350241
{txt}{space 32} {c |}
{space 7}empmediatortxt#agreenudge {c |}
(Variant 1) S1.1. people less#1  {c |}{col 34}{res}{space 2} .9090067{col 46}{space 2} .5502466{col 57}{space 1}    1.65{col 66}{space 3}0.099{col 74}{space 4}-.1714595{col 87}{space 3} 1.989473
{txt}(Variant 3) S1.3. people more#1  {c |}{col 34}{res}{space 2} 1.877142{col 46}{space 2}  .478439{col 57}{space 1}    3.92{col 66}{space 3}0.000{col 74}{space 4} .9376776{col 87}{space 3} 2.816607
{txt}{space 32} {c |}
{space 27}_cons {c |}{col 34}{res}{space 2} 5.807673{col 46}{space 2} .2808635{col 57}{space 1}   20.68{col 66}{space 3}0.000{col 74}{space 4} 5.256168{col 87}{space 3} 6.359177
{txt}{hline 33}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revempmediatorcheck ib3.humanimaltxt ib2.empmediatortxt##agreenudge if q123order==3, robust

{txt}Linear regression                               Number of obs     = {res}       631
                                                {txt}F(7, 623)         =  {res}    10.37
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1010
                                                {txt}Root MSE          =    {res} 2.1547

{txt}{hline 33}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 34}{c |}{col 46}    Robust
{col 1}             revempmediatorcheck{col 34}{c |} Coefficient{col 46}  std. err.{col 58}      t{col 66}   P>|t|{col 74}     [95% con{col 87}f. interval]
{hline 33}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}humanimaltxt {c |}
{space 20}Treatment 1  {c |}{col 34}{res}{space 2}   .19631{col 46}{space 2} .2119713{col 57}{space 1}    0.93{col 66}{space 3}0.355{col 74}{space 4}-.2199547{col 87}{space 3} .6125748
{txt}{space 20}Treatment 2  {c |}{col 34}{res}{space 2} .2568502{col 46}{space 2} .2087265{col 57}{space 1}    1.23{col 66}{space 3}0.219{col 74}{space 4}-.1530426{col 87}{space 3}  .666743
{txt}{space 32} {c |}
{space 18}empmediatortxt {c |}
{space 2}(Variant 1) S1.1. people less  {c |}{col 34}{res}{space 2}-.4024123{col 46}{space 2} .3363018{col 57}{space 1}   -1.20{col 66}{space 3}0.232{col 74}{space 4}-1.062835{col 87}{space 3} .2580102
{txt}{space 2}(Variant 3) S1.3. people more  {c |}{col 34}{res}{space 2} -1.59543{col 46}{space 2} .3475683{col 57}{space 1}   -4.59{col 66}{space 3}0.000{col 74}{space 4}-2.277978{col 87}{space 3}-.9128831
{txt}{space 32} {c |}
{space 20}1.agreenudge {c |}{col 34}{res}{space 2}-1.566097{col 46}{space 2} .3562388{col 57}{space 1}   -4.40{col 66}{space 3}0.000{col 74}{space 4}-2.265672{col 87}{space 3}-.8665232
{txt}{space 32} {c |}
{space 7}empmediatortxt#agreenudge {c |}
(Variant 1) S1.1. people less#1  {c |}{col 34}{res}{space 2} .0064961{col 46}{space 2} .4968212{col 57}{space 1}    0.01{col 66}{space 3}0.990{col 74}{space 4}-.9691509{col 87}{space 3} .9821431
{txt}(Variant 3) S1.3. people more#1  {c |}{col 34}{res}{space 2} 3.162489{col 46}{space 2} .4999109{col 57}{space 1}    6.33{col 66}{space 3}0.000{col 74}{space 4} 2.180774{col 87}{space 3} 4.144204
{txt}{space 32} {c |}
{space 27}_cons {c |}{col 34}{res}{space 2} 6.304757{col 46}{space 2}  .329353{col 57}{space 1}   19.14{col 66}{space 3}0.000{col 74}{space 4} 5.657981{col 87}{space 3} 6.951534
{txt}{hline 33}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Empathy and Human vs Animal Suffering (OLS Regression Figure)
. 
. reg revempmediatorcheck ib3.humanimaltxt ib2.empmediatortxt##agreenudge if q123order==2, robust

{txt}Linear regression                               Number of obs     = {res}       661
                                                {txt}F(7, 653)         =  {res}     3.88
                                                {txt}Prob > F          = {res}    0.0004
                                                {txt}R-squared         = {res}    0.0414
                                                {txt}Root MSE          =    {res} 2.3203

{txt}{hline 33}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 34}{c |}{col 46}    Robust
{col 1}             revempmediatorcheck{col 34}{c |} Coefficient{col 46}  std. err.{col 58}      t{col 66}   P>|t|{col 74}     [95% con{col 87}f. interval]
{hline 33}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}humanimaltxt {c |}
{space 20}Treatment 1  {c |}{col 34}{res}{space 2} .5043044{col 46}{space 2} .2247991{col 57}{space 1}    2.24{col 66}{space 3}0.025{col 74}{space 4} .0628881{col 87}{space 3} .9457207
{txt}{space 20}Treatment 2  {c |}{col 34}{res}{space 2} .0021675{col 46}{space 2} .2101188{col 57}{space 1}    0.01{col 66}{space 3}0.992{col 74}{space 4}-.4104224{col 87}{space 3} .4147574
{txt}{space 32} {c |}
{space 18}empmediatortxt {c |}
{space 2}(Variant 1) S1.1. people less  {c |}{col 34}{res}{space 2}-1.035627{col 46}{space 2} .3024231{col 57}{space 1}   -3.42{col 66}{space 3}0.001{col 74}{space 4}-1.629466{col 87}{space 3}-.4417878
{txt}{space 2}(Variant 3) S1.3. people more  {c |}{col 34}{res}{space 2}-1.241612{col 46}{space 2} .3266894{col 57}{space 1}   -3.80{col 66}{space 3}0.000{col 74}{space 4}-1.883101{col 87}{space 3}-.6001237
{txt}{space 32} {c |}
{space 20}1.agreenudge {c |}{col 34}{res}{space 2}-1.034992{col 46}{space 2} .3564713{col 57}{space 1}   -2.90{col 66}{space 3}0.004{col 74}{space 4}-1.734961{col 87}{space 3}-.3350241
{txt}{space 32} {c |}
{space 7}empmediatortxt#agreenudge {c |}
(Variant 1) S1.1. people less#1  {c |}{col 34}{res}{space 2} .9090067{col 46}{space 2} .5502466{col 57}{space 1}    1.65{col 66}{space 3}0.099{col 74}{space 4}-.1714595{col 87}{space 3} 1.989473
{txt}(Variant 3) S1.3. people more#1  {c |}{col 34}{res}{space 2} 1.877142{col 46}{space 2}  .478439{col 57}{space 1}    3.92{col 66}{space 3}0.000{col 74}{space 4} .9376776{col 87}{space 3} 2.816607
{txt}{space 32} {c |}
{space 27}_cons {c |}{col 34}{res}{space 2} 5.807673{col 46}{space 2} .2808635{col 57}{space 1}   20.68{col 66}{space 3}0.000{col 74}{space 4} 5.256168{col 87}{space 3} 6.359177
{txt}{hline 33}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. margins ib2.empmediatortxt#agreenudge if humanimaltxt==1
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:201}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 34}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 35}{c |}{col 47} Delta-method
{col 35}{c |}     Margin{col 47}   std. err.{col 59}      t{col 67}   P>|t|{col 75}     [95% con{col 88}f. interval]
{hline 34}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}empmediatortxt#agreenudge {c |}
{space 1}(Variant 1) S1.1. people less#0  {c |}{col 35}{res}{space 2}  5.27635{col 47}{space 2} .2168292{col 58}{space 1}   24.33{col 67}{space 3}0.000{col 75}{space 4} 4.850583{col 88}{space 3} 5.702117
{txt}{space 1}(Variant 1) S1.1. people less#1  {c |}{col 35}{res}{space 2} 5.150364{col 47}{space 2} .4415879{col 58}{space 1}   11.66{col 67}{space 3}0.000{col 75}{space 4} 4.283261{col 88}{space 3} 6.017468
{txt}(Variant 2) S1.2. people equal#0  {c |}{col 35}{res}{space 2} 6.311977{col 47}{space 2} .2979263{col 58}{space 1}   21.19{col 67}{space 3}0.000{col 75}{space 4} 5.726968{col 88}{space 3} 6.896986
{txt}(Variant 2) S1.2. people equal#1  {c |}{col 35}{res}{space 2} 5.276985{col 47}{space 2} .2960478{col 58}{space 1}   17.82{col 67}{space 3}0.000{col 75}{space 4} 4.695664{col 88}{space 3} 5.858305
{txt}{space 1}(Variant 3) S1.3. people more#0  {c |}{col 35}{res}{space 2} 5.070365{col 47}{space 2} .2321625{col 58}{space 1}   21.84{col 67}{space 3}0.000{col 75}{space 4}  4.61449{col 88}{space 3}  5.52624
{txt}{space 1}(Variant 3) S1.3. people more#1  {c |}{col 35}{res}{space 2} 5.912515{col 47}{space 2}  .301077{col 58}{space 1}   19.64{col 67}{space 3}0.000{col 75}{space 4} 5.321319{col 88}{space 3} 6.503711
{txt}{hline 34}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot 
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:empmediatortxt agreenudge}{p_end}
{res}{txt}
{com}. 
. *Note additional formatting required
. 
. *Experiment 2
. 
. *Experiment 2 Balance Tests
. 
. iebaltab ownpets dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income employment urban_rural region4, groupvar(humanimalaidtxt) savexlsx(balance2)

{res}{phang}Balance table saved in Excel format to: {browse "balance2.xlsx":balance2.xlsx}{p_end}
{txt}
{com}. 
. *Experiment 2 Robustness Checks
. 
. *Experiment 2 – Human versus Animal Resource Allocation (OLS Regression)
. 
. reg revaidmediatorcheck ib3.humanimalaidtxt, robust

{txt}Linear regression                               Number of obs     = {res}     1,971
                                                {txt}F(2, 1968)        =  {res}     0.99
                                                {txt}Prob > F          = {res}    0.3726
                                                {txt}R-squared         = {res}    0.0010
                                                {txt}Root MSE          =    {res} 2.1329

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}revaidmediato~k{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimalaidtxt {c |}
{space 3}Treatment 1  {c |}{col 17}{res}{space 2}-.0433271{col 29}{space 2} .1192926{col 40}{space 1}   -0.36{col 49}{space 3}0.716{col 57}{space 4}-.2772801{col 70}{space 3}  .190626
{txt}{space 3}Treatment 2  {c |}{col 17}{res}{space 2}-.1588116{col 29}{space 2} .1178514{col 40}{space 1}   -1.35{col 49}{space 3}0.178{col 57}{space 4}-.3899382{col 70}{space 3}  .072315
{txt}{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2} 5.703988{col 29}{space 2} .0857062{col 40}{space 1}   66.55{col 49}{space 3}0.000{col 57}{space 4} 5.535903{col 70}{space 3} 5.872072
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg providemoral ib3.humanimalaidtxt, robust

{txt}Linear regression                               Number of obs     = {res}     1,945
                                                {txt}F(2, 1942)        =  {res}     0.35
                                                {txt}Prob > F          = {res}    0.7031
                                                {txt}R-squared         = {res}    0.0004
                                                {txt}Root MSE          =    {res}  3.121

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}   providemoral{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimalaidtxt {c |}
{space 3}Treatment 1  {c |}{col 17}{res}{space 2}-.1261303{col 29}{space 2} .1727352{col 40}{space 1}   -0.73{col 49}{space 3}0.465{col 57}{space 4}-.4648962{col 70}{space 3} .2126355
{txt}{space 3}Treatment 2  {c |}{col 17}{res}{space 2}-.1253858{col 29}{space 2} .1736059{col 40}{space 1}   -0.72{col 49}{space 3}0.470{col 57}{space 4}-.4658594{col 70}{space 3} .2150878
{txt}{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2} 5.624611{col 29}{space 2}  .122093{col 40}{space 1}   46.07{col 49}{space 3}0.000{col 57}{space 4} 5.385163{col 70}{space 3} 5.864058
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg selfhelp ib3.humanimalaidtxt, robust

{txt}Linear regression                               Number of obs     = {res}     1,965
                                                {txt}F(2, 1962)        =  {res}     0.11
                                                {txt}Prob > F          = {res}    0.9003
                                                {txt}R-squared         = {res}    0.0001
                                                {txt}Root MSE          =    {res} 2.6678

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}       selfhelp{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimalaidtxt {c |}
{space 3}Treatment 1  {c |}{col 17}{res}{space 2}  -.02375{col 29}{space 2}  .147604{col 40}{space 1}   -0.16{col 49}{space 3}0.872{col 57}{space 4}-.3132271{col 70}{space 3}  .265727
{txt}{space 3}Treatment 2  {c |}{col 17}{res}{space 2} .0431255{col 29}{space 2} .1462563{col 40}{space 1}    0.29{col 49}{space 3}0.768{col 57}{space 4}-.2437085{col 70}{space 3} .3299595
{txt}{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2} 8.116564{col 29}{space 2} .1029782{col 40}{space 1}   78.82{col 49}{space 3}0.000{col 57}{space 4} 7.914606{col 70}{space 3} 8.318523
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg giveidp100rnd ib3.humanimalaidtxt, robust

{txt}Linear regression                               Number of obs     = {res}     1,981
                                                {txt}F(2, 1978)        =  {res}     3.12
                                                {txt}Prob > F          = {res}    0.0445
                                                {txt}R-squared         = {res}    0.0032
                                                {txt}Root MSE          =    {res} 2.2547

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}  giveidp100rnd{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimalaidtxt {c |}
{space 3}Treatment 1  {c |}{col 17}{res}{space 2}-.0547203{col 29}{space 2} .1222233{col 40}{space 1}   -0.45{col 49}{space 3}0.654{col 57}{space 4}-.2944203{col 70}{space 3} .1849796
{txt}{space 3}Treatment 2  {c |}{col 17}{res}{space 2}-.2953914{col 29}{space 2} .1250623{col 40}{space 1}   -2.36{col 49}{space 3}0.018{col 57}{space 4} -.540659{col 70}{space 3}-.0501238
{txt}{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2} 6.221884{col 29}{space 2} .0864327{col 40}{space 1}   71.99{col 49}{space 3}0.000{col 57}{space 4} 6.052376{col 70}{space 3} 6.391393
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Experiment 2 – Extended Controls (OLS Regression)
. 
. reg revaidmediatorcheck  ib3.humanimalaidtxt i.ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4 , robust

{txt}Linear regression                               Number of obs     = {res}     1,894
                                                {txt}F(30, 1863)       =  {res}     3.90
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0572
                                                {txt}Root MSE          =    {res} 2.0795

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                    revaidmediatorcheck{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      t{col 73}   P>|t|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}humanimalaidtxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2} .0123508{col 53}{space 2}  .120173{col 64}{space 1}    0.10{col 73}{space 3}0.918{col 81}{space 4}-.2233371{col 94}{space 3} .2480386
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2}-.1418567{col 53}{space 2}  .118046{col 64}{space 1}   -1.20{col 73}{space 3}0.230{col 81}{space 4} -.373373{col 94}{space 3} .0896595
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}-.6291486{col 53}{space 2} .1170021{col 64}{space 1}   -5.38{col 73}{space 3}0.000{col 81}{space 4}-.8586175{col 94}{space 3}-.3996797
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2}  .215766{col 53}{space 2} .4087513{col 64}{space 1}    0.53{col 73}{space 3}0.598{col 81}{space 4}-.5858926{col 94}{space 3} 1.017425
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2}-.6363796{col 53}{space 2} .1492919{col 64}{space 1}   -4.26{col 73}{space 3}0.000{col 81}{space 4}-.9291766{col 94}{space 3}-.3435825
{txt}{space 39} {c |}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2}-.0140629{col 53}{space 2} .1334215{col 64}{space 1}   -0.11{col 73}{space 3}0.916{col 81}{space 4}-.2757342{col 94}{space 3} .2476085
{txt}{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2}-.2340964{col 53}{space 2} .1760032{col 64}{space 1}   -1.33{col 73}{space 3}0.184{col 81}{space 4}-.5792807{col 94}{space 3} .1110879
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2}-.0700919{col 53}{space 2} .0976132{col 64}{space 1}   -0.72{col 73}{space 3}0.473{col 81}{space 4}-.2615346{col 94}{space 3} .1213507
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2}-.1141664{col 53}{space 2} .1438998{col 64}{space 1}   -0.79{col 73}{space 3}0.428{col 81}{space 4}-.3963881{col 94}{space 3} .1680553
{txt}{space 30}displaced {c |}{col 41}{res}{space 2} .0812191{col 53}{space 2} .1461778{col 64}{space 1}    0.56{col 73}{space 3}0.579{col 81}{space 4}-.2054705{col 94}{space 3} .3679087
{txt}{space 30}ukrainian {c |}{col 41}{res}{space 2} .1424822{col 53}{space 2} .3026328{col 64}{space 1}    0.47{col 73}{space 3}0.638{col 81}{space 4}-.4510528{col 94}{space 3} .7360172
{txt}{space 32}russian {c |}{col 41}{res}{space 2} -.152289{col 53}{space 2} .4000771{col 64}{space 1}   -0.38{col 73}{space 3}0.704{col 81}{space 4}-.9369354{col 94}{space 3} .6323574
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2} .1403052{col 53}{space 2} .1581766{col 64}{space 1}    0.89{col 73}{space 3}0.375{col 81}{space 4}-.1699169{col 94}{space 3} .4505272
{txt}{space 33}female {c |}{col 41}{res}{space 2}-.4956008{col 53}{space 2} .1052469{col 64}{space 1}   -4.71{col 73}{space 3}0.000{col 81}{space 4} -.702015{col 94}{space 3}-.2891867
{txt}{space 36}age {c |}{col 41}{res}{space 2}-.0021269{col 53}{space 2} .0045857{col 64}{space 1}   -0.46{col 73}{space 3}0.643{col 81}{space 4}-.0111205{col 94}{space 3} .0068668
{txt}{space 30}education {c |}{col 41}{res}{space 2} .1136513{col 53}{space 2}  .039377{col 64}{space 1}    2.89{col 73}{space 3}0.004{col 81}{space 4} .0364237{col 94}{space 3} .1908789
{txt}{space 33}income {c |}{col 41}{res}{space 2} .0451311{col 53}{space 2} .0623518{col 64}{space 1}    0.72{col 73}{space 3}0.469{col 81}{space 4}-.0771556{col 94}{space 3} .1674178
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2}-.0850585{col 53}{space 2} .2255304{col 64}{space 1}   -0.38{col 73}{space 3}0.706{col 81}{space 4}-.5273774{col 94}{space 3} .3572603
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2}  .173383{col 53}{space 2} .1971017{col 64}{space 1}    0.88{col 73}{space 3}0.379{col 81}{space 4}-.2131803{col 94}{space 3} .5599463
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2} .5333753{col 53}{space 2} .2863527{col 64}{space 1}    1.86{col 73}{space 3}0.063{col 81}{space 4}-.0282306{col 94}{space 3} 1.094981
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2} .3239166{col 53}{space 2} .2621957{col 64}{space 1}    1.24{col 73}{space 3}0.217{col 81}{space 4}-.1903116{col 94}{space 3} .8381448
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2}   .37819{col 53}{space 2}   .43835{col 64}{space 1}    0.86{col 73}{space 3}0.388{col 81}{space 4}-.4815186{col 94}{space 3} 1.237899
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2}  .058078{col 53}{space 2} .2264012{col 64}{space 1}    0.26{col 73}{space 3}0.798{col 81}{space 4}-.3859486{col 94}{space 3} .5021047
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2}  .071009{col 53}{space 2} .2006952{col 64}{space 1}    0.35{col 73}{space 3}0.724{col 81}{space 4}-.3226021{col 94}{space 3}   .46462
{txt}{space 31}Student  {c |}{col 41}{res}{space 2} .0471548{col 53}{space 2} .3726809{col 64}{space 1}    0.13{col 73}{space 3}0.899{col 81}{space 4}-.6837613{col 94}{space 3} .7780709
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2} .0780236{col 53}{space 2} .3485742{col 64}{space 1}    0.22{col 73}{space 3}0.823{col 81}{space 4}-.6056133{col 94}{space 3} .7616606
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2} .2611757{col 53}{space 2} .1306348{col 64}{space 1}    2.00{col 73}{space 3}0.046{col 81}{space 4} .0049697{col 94}{space 3} .5173816
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2} .1970514{col 53}{space 2} .1902124{col 64}{space 1}    1.04{col 73}{space 3}0.300{col 81}{space 4}-.1760005{col 94}{space 3} .5701032
{txt}{space 31}Central  {c |}{col 41}{res}{space 2} .0267503{col 53}{space 2} .1626826{col 64}{space 1}    0.16{col 73}{space 3}0.869{col 81}{space 4}-.2923089{col 94}{space 3} .3458096
{txt}{space 33}South  {c |}{col 41}{res}{space 2}  .101764{col 53}{space 2} .1675132{col 64}{space 1}    0.61{col 73}{space 3}0.544{col 81}{space 4}-.2267693{col 94}{space 3} .4302973
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2} 4.946239{col 53}{space 2} .6013667{col 64}{space 1}    8.22{col 73}{space 3}0.000{col 81}{space 4} 3.766815{col 94}{space 3} 6.125662
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg providemoral ib3.humanimalaidtxt i.ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4 , robust

{txt}Linear regression                               Number of obs     = {res}     1,875
                                                {txt}F(30, 1844)       =  {res}     3.71
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0562
                                                {txt}Root MSE          =    {res}  3.045

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                           providemoral{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      t{col 73}   P>|t|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}humanimalaidtxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2}-.0592966{col 53}{space 2} .1733689{col 64}{space 1}   -0.34{col 73}{space 3}0.732{col 81}{space 4}-.3993166{col 94}{space 3} .2807235
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2}-.1505231{col 53}{space 2} .1731443{col 64}{space 1}   -0.87{col 73}{space 3}0.385{col 81}{space 4}-.4901025{col 94}{space 3} .1890563
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}-.8079595{col 53}{space 2} .1637712{col 64}{space 1}   -4.93{col 73}{space 3}0.000{col 81}{space 4}-1.129156{col 94}{space 3} -.486763
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2} .5929839{col 53}{space 2}  .556282{col 64}{space 1}    1.07{col 73}{space 3}0.287{col 81}{space 4}-.4980249{col 94}{space 3} 1.683993
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2}-.5429782{col 53}{space 2} .2320706{col 64}{space 1}   -2.34{col 73}{space 3}0.019{col 81}{space 4} -.998127{col 94}{space 3}-.0878295
{txt}{space 39} {c |}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2} .1796108{col 53}{space 2} .1862632{col 64}{space 1}    0.96{col 73}{space 3}0.335{col 81}{space 4}-.1856981{col 94}{space 3} .5449197
{txt}{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2} .1166382{col 53}{space 2} .2541729{col 64}{space 1}    0.46{col 73}{space 3}0.646{col 81}{space 4}-.3818586{col 94}{space 3} .6151351
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2}-.0864357{col 53}{space 2} .1450016{col 64}{space 1}   -0.60{col 73}{space 3}0.551{col 81}{space 4}-.3708203{col 94}{space 3} .1979489
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2} .2357368{col 53}{space 2} .2010798{col 64}{space 1}    1.17{col 73}{space 3}0.241{col 81}{space 4}-.1586313{col 94}{space 3} .6301049
{txt}{space 30}displaced {c |}{col 41}{res}{space 2}-.0111685{col 53}{space 2} .2268379{col 64}{space 1}   -0.05{col 73}{space 3}0.961{col 81}{space 4}-.4560547{col 94}{space 3} .4337176
{txt}{space 30}ukrainian {c |}{col 41}{res}{space 2}-.1271065{col 53}{space 2} .3866711{col 64}{space 1}   -0.33{col 73}{space 3}0.742{col 81}{space 4}-.8854656{col 94}{space 3} .6312526
{txt}{space 32}russian {c |}{col 41}{res}{space 2} .3347484{col 53}{space 2}  .590983{col 64}{space 1}    0.57{col 73}{space 3}0.571{col 81}{space 4}-.8243178{col 94}{space 3} 1.493815
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2}-.0326054{col 53}{space 2}  .238976{col 64}{space 1}   -0.14{col 73}{space 3}0.891{col 81}{space 4}-.5012973{col 94}{space 3} .4360865
{txt}{space 33}female {c |}{col 41}{res}{space 2}-.8797776{col 53}{space 2} .1540451{col 64}{space 1}   -5.71{col 73}{space 3}0.000{col 81}{space 4}-1.181899{col 94}{space 3}-.5776565
{txt}{space 36}age {c |}{col 41}{res}{space 2} .0174366{col 53}{space 2}  .006545{col 64}{space 1}    2.66{col 73}{space 3}0.008{col 81}{space 4} .0046003{col 94}{space 3} .0302729
{txt}{space 30}education {c |}{col 41}{res}{space 2} .0418037{col 53}{space 2} .0575049{col 64}{space 1}    0.73{col 73}{space 3}0.467{col 81}{space 4}-.0709779{col 94}{space 3} .1545853
{txt}{space 33}income {c |}{col 41}{res}{space 2}-.0670353{col 53}{space 2} .0930721{col 64}{space 1}   -0.72{col 73}{space 3}0.471{col 81}{space 4}-.2495731{col 94}{space 3} .1155026
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2}-.1887761{col 53}{space 2} .3553859{col 64}{space 1}   -0.53{col 73}{space 3}0.595{col 81}{space 4}-.8857771{col 94}{space 3} .5082249
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2}-.1981806{col 53}{space 2} .2730443{col 64}{space 1}   -0.73{col 73}{space 3}0.468{col 81}{space 4}-.7336891{col 94}{space 3} .3373278
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2} .7828213{col 53}{space 2} .3909748{col 64}{space 1}    2.00{col 73}{space 3}0.045{col 81}{space 4} .0160214{col 94}{space 3} 1.549621
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2} .0023106{col 53}{space 2} .3600514{col 64}{space 1}    0.01{col 73}{space 3}0.995{col 81}{space 4}-.7038406{col 94}{space 3} .7084618
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2} .3162553{col 53}{space 2}  .563773{col 64}{space 1}    0.56{col 73}{space 3}0.575{col 81}{space 4}-.7894452{col 94}{space 3} 1.421956
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2}-.2204111{col 53}{space 2}  .347652{col 64}{space 1}   -0.63{col 73}{space 3}0.526{col 81}{space 4} -.902244{col 94}{space 3} .4614218
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2} .0078989{col 53}{space 2} .2792763{col 64}{space 1}    0.03{col 73}{space 3}0.977{col 81}{space 4}-.5398321{col 94}{space 3} .5556299
{txt}{space 31}Student  {c |}{col 41}{res}{space 2} .6764654{col 53}{space 2} .6353572{col 64}{space 1}    1.06{col 73}{space 3}0.287{col 81}{space 4}-.5696298{col 94}{space 3} 1.922561
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2} .5225789{col 53}{space 2} .4168278{col 64}{space 1}    1.25{col 73}{space 3}0.210{col 81}{space 4}-.2949251{col 94}{space 3} 1.340083
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2} .3407882{col 53}{space 2} .2049034{col 64}{space 1}    1.66{col 73}{space 3}0.096{col 81}{space 4}-.0610789{col 94}{space 3} .7426552
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2}-.0407896{col 53}{space 2} .2798717{col 64}{space 1}   -0.15{col 73}{space 3}0.884{col 81}{space 4}-.5896882{col 94}{space 3} .5081091
{txt}{space 31}Central  {c |}{col 41}{res}{space 2}-.1240658{col 53}{space 2} .2421245{col 64}{space 1}   -0.51{col 73}{space 3}0.608{col 81}{space 4}-.5989328{col 94}{space 3} .3508011
{txt}{space 33}South  {c |}{col 41}{res}{space 2}-.1462493{col 53}{space 2} .2497144{col 64}{space 1}   -0.59{col 73}{space 3}0.558{col 81}{space 4} -.636002{col 94}{space 3} .3435033
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2} 5.368221{col 53}{space 2} .8603386{col 64}{space 1}    6.24{col 73}{space 3}0.000{col 81}{space 4} 3.680881{col 94}{space 3} 7.055561
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg selfhelp ib3.humanimalaidtxt i.ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4 , robust

{txt}Linear regression                               Number of obs     = {res}     1,892
                                                {txt}F(30, 1861)       =  {res}     1.00
                                                {txt}Prob > F          = {res}    0.4622
                                                {txt}R-squared         = {res}    0.0163
                                                {txt}Root MSE          =    {res} 2.6546

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                               selfhelp{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      t{col 73}   P>|t|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}humanimalaidtxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2}-.0728529{col 53}{space 2} .1514292{col 64}{space 1}   -0.48{col 73}{space 3}0.631{col 81}{space 4}-.3698418{col 94}{space 3}  .224136
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2} .0204285{col 53}{space 2} .1475155{col 64}{space 1}    0.14{col 73}{space 3}0.890{col 81}{space 4}-.2688847{col 94}{space 3} .3097417
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2} .3234505{col 53}{space 2} .1445467{col 64}{space 1}    2.24{col 73}{space 3}0.025{col 81}{space 4} .0399599{col 94}{space 3} .6069412
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2} .4371185{col 53}{space 2}  .450541{col 64}{space 1}    0.97{col 73}{space 3}0.332{col 81}{space 4}-.4465004{col 94}{space 3} 1.320737
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2} .2153133{col 53}{space 2} .1978172{col 64}{space 1}    1.09{col 73}{space 3}0.277{col 81}{space 4}-.1726537{col 94}{space 3} .6032803
{txt}{space 39} {c |}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2}-.2413458{col 53}{space 2} .1634055{col 64}{space 1}   -1.48{col 73}{space 3}0.140{col 81}{space 4}-.5618232{col 94}{space 3} .0791315
{txt}{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2}-.1162549{col 53}{space 2} .2254572{col 64}{space 1}   -0.52{col 73}{space 3}0.606{col 81}{space 4}-.5584306{col 94}{space 3} .3259207
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2} .0050478{col 53}{space 2}  .126612{col 64}{space 1}    0.04{col 73}{space 3}0.968{col 81}{space 4}-.2432687{col 94}{space 3} .2533644
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2} .0494136{col 53}{space 2} .1830084{col 64}{space 1}    0.27{col 73}{space 3}0.787{col 81}{space 4}-.3095098{col 94}{space 3}  .408337
{txt}{space 30}displaced {c |}{col 41}{res}{space 2} .1447341{col 53}{space 2}  .191824{col 64}{space 1}    0.75{col 73}{space 3}0.451{col 81}{space 4}-.2314787{col 94}{space 3}  .520947
{txt}{space 30}ukrainian {c |}{col 41}{res}{space 2} .9020853{col 53}{space 2} .4104524{col 64}{space 1}    2.20{col 73}{space 3}0.028{col 81}{space 4} .0970899{col 94}{space 3} 1.707081
{txt}{space 32}russian {c |}{col 41}{res}{space 2} .3890891{col 53}{space 2} .5678924{col 64}{space 1}    0.69{col 73}{space 3}0.493{col 81}{space 4}-.7246839{col 94}{space 3} 1.502862
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2} .1554855{col 53}{space 2} .2156653{col 64}{space 1}    0.72{col 73}{space 3}0.471{col 81}{space 4}-.2674857{col 94}{space 3} .5784568
{txt}{space 33}female {c |}{col 41}{res}{space 2} .0994698{col 53}{space 2} .1324259{col 64}{space 1}    0.75{col 73}{space 3}0.453{col 81}{space 4}-.1602491{col 94}{space 3} .3591886
{txt}{space 36}age {c |}{col 41}{res}{space 2} .0046473{col 53}{space 2} .0056239{col 64}{space 1}    0.83{col 73}{space 3}0.409{col 81}{space 4}-.0063824{col 94}{space 3} .0156771
{txt}{space 30}education {c |}{col 41}{res}{space 2} -.003833{col 53}{space 2} .0465829{col 64}{space 1}   -0.08{col 73}{space 3}0.934{col 81}{space 4}-.0951932{col 94}{space 3} .0875272
{txt}{space 33}income {c |}{col 41}{res}{space 2} .0943751{col 53}{space 2} .0812255{col 64}{space 1}    1.16{col 73}{space 3}0.245{col 81}{space 4}-.0649276{col 94}{space 3} .2536778
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2} .2668182{col 53}{space 2}  .280906{col 64}{space 1}    0.95{col 73}{space 3}0.342{col 81}{space 4}-.2841056{col 94}{space 3} .8177421
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2} .1972008{col 53}{space 2} .2434622{col 64}{space 1}    0.81{col 73}{space 3}0.418{col 81}{space 4} -.280287{col 94}{space 3} .6746886
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2} .1558848{col 53}{space 2} .3435649{col 64}{space 1}    0.45{col 73}{space 3}0.650{col 81}{space 4}-.5179282{col 94}{space 3} .8296978
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2}-.1149677{col 53}{space 2} .3257587{col 64}{space 1}   -0.35{col 73}{space 3}0.724{col 81}{space 4}-.7538585{col 94}{space 3}  .523923
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2} .4605817{col 53}{space 2} .4719238{col 64}{space 1}    0.98{col 73}{space 3}0.329{col 81}{space 4}-.4649741{col 94}{space 3} 1.386137
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2}-.0508934{col 53}{space 2} .3157402{col 64}{space 1}   -0.16{col 73}{space 3}0.872{col 81}{space 4}-.6701356{col 94}{space 3} .5683489
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2} .1434903{col 53}{space 2}  .254205{col 64}{space 1}    0.56{col 73}{space 3}0.573{col 81}{space 4}-.3550666{col 94}{space 3} .6420471
{txt}{space 31}Student  {c |}{col 41}{res}{space 2}-.3486134{col 53}{space 2} .5600277{col 64}{space 1}   -0.62{col 73}{space 3}0.534{col 81}{space 4}-1.446962{col 94}{space 3} .7497351
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2}-.3245345{col 53}{space 2} .4276122{col 64}{space 1}   -0.76{col 73}{space 3}0.448{col 81}{space 4}-1.163184{col 94}{space 3} .5141154
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2}-.2084028{col 53}{space 2} .1777796{col 64}{space 1}   -1.17{col 73}{space 3}0.241{col 81}{space 4}-.5570711{col 94}{space 3} .1402655
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2} .0972086{col 53}{space 2}   .24723{col 64}{space 1}    0.39{col 73}{space 3}0.694{col 81}{space 4}-.3876687{col 94}{space 3} .5820859
{txt}{space 31}Central  {c |}{col 41}{res}{space 2} .2066719{col 53}{space 2}  .219292{col 64}{space 1}    0.94{col 73}{space 3}0.346{col 81}{space 4}-.2234122{col 94}{space 3}  .636756
{txt}{space 33}South  {c |}{col 41}{res}{space 2} .2825619{col 53}{space 2} .2228911{col 64}{space 1}    1.27{col 73}{space 3}0.205{col 81}{space 4} -.154581{col 94}{space 3} .7197047
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2} 6.376715{col 53}{space 2} .7444284{col 64}{space 1}    8.57{col 73}{space 3}0.000{col 81}{space 4} 4.916713{col 94}{space 3} 7.836718
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg giveidp100rnd ib3.humanimalaidtxt i.ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4 , robust

{txt}Linear regression                               Number of obs     = {res}     1,906
                                                {txt}F(30, 1875)       =  {res}     8.22
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1165
                                                {txt}Root MSE          =    {res} 2.1235

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                          giveidp100rnd{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      t{col 73}   P>|t|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}humanimalaidtxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2}-.0500526{col 53}{space 2} .1186712{col 64}{space 1}   -0.42{col 73}{space 3}0.673{col 81}{space 4}-.2827941{col 94}{space 3} .1826889
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2}-.2886357{col 53}{space 2} .1211265{col 64}{space 1}   -2.38{col 73}{space 3}0.017{col 81}{space 4}-.5261927{col 94}{space 3}-.0510787
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}-.7056667{col 53}{space 2} .1136498{col 64}{space 1}   -6.21{col 73}{space 3}0.000{col 81}{space 4}-.9285601{col 94}{space 3}-.4827734
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2} .1226369{col 53}{space 2} .3137812{col 64}{space 1}    0.39{col 73}{space 3}0.696{col 81}{space 4}-.4927602{col 94}{space 3} .7380339
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2}-.6936769{col 53}{space 2} .1619344{col 64}{space 1}   -4.28{col 73}{space 3}0.000{col 81}{space 4}-1.011268{col 94}{space 3}-.3760862
{txt}{space 39} {c |}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2} .0372555{col 53}{space 2} .1367914{col 64}{space 1}    0.27{col 73}{space 3}0.785{col 81}{space 4}-.2310238{col 94}{space 3} .3055349
{txt}{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2}-.4411685{col 53}{space 2} .1991448{col 64}{space 1}   -2.22{col 73}{space 3}0.027{col 81}{space 4}-.8317373{col 94}{space 3}-.0505996
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2}-.0738947{col 53}{space 2} .0994084{col 64}{space 1}   -0.74{col 73}{space 3}0.457{col 81}{space 4}-.2688575{col 94}{space 3} .1210681
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2} .1101536{col 53}{space 2} .1479329{col 64}{space 1}    0.74{col 73}{space 3}0.457{col 81}{space 4}-.1799768{col 94}{space 3} .4002841
{txt}{space 30}displaced {c |}{col 41}{res}{space 2} .0792247{col 53}{space 2}  .154533{col 64}{space 1}    0.51{col 73}{space 3}0.608{col 81}{space 4}  -.22385{col 94}{space 3} .3822993
{txt}{space 30}ukrainian {c |}{col 41}{res}{space 2} .1027038{col 53}{space 2} .2617025{col 64}{space 1}    0.39{col 73}{space 3}0.695{col 81}{space 4} -.410555{col 94}{space 3} .6159626
{txt}{space 32}russian {c |}{col 41}{res}{space 2}-.1910496{col 53}{space 2} .4205738{col 64}{space 1}   -0.45{col 73}{space 3}0.650{col 81}{space 4}-1.015892{col 94}{space 3} .6337923
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2} .1950049{col 53}{space 2} .1614766{col 64}{space 1}    1.21{col 73}{space 3}0.227{col 81}{space 4}-.1216878{col 94}{space 3} .5116976
{txt}{space 33}female {c |}{col 41}{res}{space 2}-1.176836{col 53}{space 2} .1055228{col 64}{space 1}  -11.15{col 73}{space 3}0.000{col 81}{space 4} -1.38379{col 94}{space 3}-.9698812
{txt}{space 36}age {c |}{col 41}{res}{space 2} .0074956{col 53}{space 2} .0047321{col 64}{space 1}    1.58{col 73}{space 3}0.113{col 81}{space 4}-.0017852{col 94}{space 3} .0167763
{txt}{space 30}education {c |}{col 41}{res}{space 2} .0756929{col 53}{space 2} .0388664{col 64}{space 1}    1.95{col 73}{space 3}0.052{col 81}{space 4}-.0005329{col 94}{space 3} .1519188
{txt}{space 33}income {c |}{col 41}{res}{space 2} .0271004{col 53}{space 2} .0611748{col 64}{space 1}    0.44{col 73}{space 3}0.658{col 81}{space 4}-.0928775{col 94}{space 3} .1470783
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2} .4012911{col 53}{space 2} .2483893{col 64}{space 1}    1.62{col 73}{space 3}0.106{col 81}{space 4}-.0858575{col 94}{space 3} .8884396
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2} .3428028{col 53}{space 2} .1929977{col 64}{space 1}    1.78{col 73}{space 3}0.076{col 81}{space 4}  -.03571{col 94}{space 3} .7213157
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2} .8625407{col 53}{space 2} .2496901{col 64}{space 1}    3.45{col 73}{space 3}0.001{col 81}{space 4}  .372841{col 94}{space 3}  1.35224
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2} .5178153{col 53}{space 2} .2347505{col 64}{space 1}    2.21{col 73}{space 3}0.028{col 81}{space 4} .0574156{col 94}{space 3} .9782149
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2} .1911962{col 53}{space 2} .4011203{col 64}{space 1}    0.48{col 73}{space 3}0.634{col 81}{space 4}-.5954931{col 94}{space 3} .9778854
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2} .2461546{col 53}{space 2} .2451418{col 64}{space 1}    1.00{col 73}{space 3}0.315{col 81}{space 4}-.2346248{col 94}{space 3} .7269341
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2} .1668997{col 53}{space 2} .1957769{col 64}{space 1}    0.85{col 73}{space 3}0.394{col 81}{space 4}-.2170638{col 94}{space 3} .5508632
{txt}{space 31}Student  {c |}{col 41}{res}{space 2} .3869021{col 53}{space 2} .3272837{col 64}{space 1}    1.18{col 73}{space 3}0.237{col 81}{space 4}-.2549766{col 94}{space 3} 1.028781
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2} .4473224{col 53}{space 2} .3263623{col 64}{space 1}    1.37{col 73}{space 3}0.171{col 81}{space 4}-.1927492{col 94}{space 3} 1.087394
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2} .1765091{col 53}{space 2} .1372293{col 64}{space 1}    1.29{col 73}{space 3}0.199{col 81}{space 4} -.092629{col 94}{space 3} .4456472
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2} .3858664{col 53}{space 2} .1867536{col 64}{space 1}    2.07{col 73}{space 3}0.039{col 81}{space 4} .0195996{col 94}{space 3} .7521331
{txt}{space 31}Central  {c |}{col 41}{res}{space 2} .0932289{col 53}{space 2} .1655859{col 64}{space 1}    0.56{col 73}{space 3}0.573{col 81}{space 4}-.2315231{col 94}{space 3} .4179809
{txt}{space 33}South  {c |}{col 41}{res}{space 2} .2429868{col 53}{space 2} .1735286{col 64}{space 1}    1.40{col 73}{space 3}0.162{col 81}{space 4}-.0973427{col 94}{space 3} .5833163
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2} 5.569271{col 53}{space 2} .5988393{col 64}{space 1}    9.30{col 73}{space 3}0.000{col 81}{space 4}  4.39481{col 94}{space 3} 6.743733
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Experiment 2 – Human versus Animal Resource Allocation (Tobit Regression)
. 
. tobit revaidmediatorcheck ib3.humanimalaidtxt, ll ul
{res}{txt}
Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-4338.1911}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log likelihood = {res:-4338.1911}  
Iteration 1:{space 2}Log likelihood = {res:-4302.3007}  
Iteration 2:{space 2}Log likelihood = {res:-4301.8113}  
Iteration 3:{space 2}Log likelihood = {res:-4301.8096}  
Iteration 4:{space 2}Log likelihood = {res:-4301.8096}  
{res}
{txt}{col 1}Tobit regression{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:1,971}
{txt}{col 53}{ralign 17:Uncensored}{col 70} = {res}{ralign 6:1,683}
{txt}{col 1}Limits: Lower = {ralign 2:{res:0}}{col 53}{ralign 17:Left-censored}{col 70} = {res}{ralign 6:70}
{txt}{col 1}        Upper = {ralign 2:{res:10}}{col 53}{ralign 17:   Right-censored}{col 70} = {res}{ralign 6:218}

{txt}{col 53}{lalign 17:LR chi2({res:2})}{col 70} = {res}{ralign 6:1.82}
{txt}{col 53}{lalign 17:Prob > chi2}{col 70} = {res}{ralign 6:0.4024}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-4301.8096}{txt}{col 53}{lalign 17:Pseudo R2}{col 70} = {res}{ralign 6:0.0002}

{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       revaidmediatorcheck{col 28}{c |} Coefficient{col 40}  Std. err.{col 52}      t{col 60}   P>|t|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}humanimalaidtxt {c |}
{space 14}Treatment 1  {c |}{col 28}{res}{space 2}-.0351568{col 40}{space 2} .1371514{col 51}{space 1}   -0.26{col 60}{space 3}0.798{col 68}{space 4} -.304134{col 81}{space 3} .2338204
{txt}{space 14}Treatment 2  {c |}{col 28}{res}{space 2}-.1754918{col 40}{space 2} .1377277{col 51}{space 1}   -1.27{col 60}{space 3}0.203{col 68}{space 4}-.4455991{col 81}{space 3} .0946155
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2}  5.78768{col 40}{space 2} .0975001{col 51}{space 1}   59.36{col 60}{space 3}0.000{col 68}{space 4} 5.596466{col 81}{space 3} 5.978894
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}var(e.revaidmediatorcheck){c |}{col 28}{res}{space 2} 6.081006{col 40}{space 2} .2209624{col 68}{space 4} 5.662741{col 81}{space 3} 6.530164
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. tobit providemoral ib3.humanimalaidtxt, ll ul
{res}{txt}
Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-4662.2892}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log likelihood = {res:-4662.2892}  
Iteration 1:{space 2}Log likelihood = {res:  -4462.64}  
Iteration 2:{space 2}Log likelihood = {res:-4460.1294}  
Iteration 3:{space 2}Log likelihood = {res:-4460.1233}  
Iteration 4:{space 2}Log likelihood = {res:-4460.1233}  
{res}
{txt}{col 1}Tobit regression{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:1,945}
{txt}{col 53}{ralign 17:Uncensored}{col 70} = {res}{ralign 6:1,256}
{txt}{col 1}Limits: Lower = {ralign 2:{res:0}}{col 53}{ralign 17:Left-censored}{col 70} = {res}{ralign 6:268}
{txt}{col 1}        Upper = {ralign 2:{res:10}}{col 53}{ralign 17:   Right-censored}{col 70} = {res}{ralign 6:421}

{txt}{col 53}{lalign 17:LR chi2({res:2})}{col 70} = {res}{ralign 6:0.73}
{txt}{col 53}{lalign 17:Prob > chi2}{col 70} = {res}{ralign 6:0.6935}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-4460.1233}{txt}{col 53}{lalign 17:Pseudo R2}{col 70} = {res}{ralign 6:0.0001}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       providemoral{col 21}{c |} Coefficient{col 33}  Std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}humanimalaidtxt {c |}
{space 7}Treatment 1  {c |}{col 21}{res}{space 2}-.2133674{col 33}{space 2}   .26621{col 44}{space 1}   -0.80{col 53}{space 3}0.423{col 61}{space 4}-.7354545{col 74}{space 3} .3087198
{txt}{space 7}Treatment 2  {c |}{col 21}{res}{space 2}-.1771643{col 33}{space 2}  .267602{col 44}{space 1}   -0.66{col 53}{space 3}0.508{col 61}{space 4}-.7019814{col 74}{space 3} .3476528
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} 5.909993{col 33}{space 2} .1895553{col 44}{space 1}   31.18{col 53}{space 3}0.000{col 61}{space 4}  5.53824{col 74}{space 3} 6.281747
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}var(e.providemoral){c |}{col 21}{res}{space 2}  21.4429{col 33}{space 2} .9614401{col 61}{space 4} 19.63786{col 74}{space 3} 23.41385
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. tobit selfhelp ib3.humanimalaidtxt, ll ul 
{res}{txt}
Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-3906.0639}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log likelihood = {res:-3906.0639}  
Iteration 1:{space 2}Log likelihood = {res:-3363.8675}  
Iteration 2:{space 2}Log likelihood = {res: -3237.623}  
Iteration 3:{space 2}Log likelihood = {res:-3225.2792}  
Iteration 4:{space 2}Log likelihood = {res:-3225.0539}  
Iteration 5:{space 2}Log likelihood = {res:-3225.0535}  
Iteration 6:{space 2}Log likelihood = {res:-3225.0535}  
{res}
{txt}{col 1}Tobit regression{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:1,965}
{txt}{col 53}{ralign 17:Uncensored}{col 70} = {res}{ralign 6:760}
{txt}{col 1}Limits: Lower = {ralign 2:{res:0}}{col 53}{ralign 17:Left-censored}{col 70} = {res}{ralign 6:66}
{txt}{col 1}        Upper = {ralign 2:{res:10}}{col 53}{ralign 17:   Right-censored}{col 70} = {res}{ralign 6:1,139}

{txt}{col 53}{lalign 17:LR chi2({res:2})}{col 70} = {res}{ralign 6:0.22}
{txt}{col 53}{lalign 17:Prob > chi2}{col 70} = {res}{ralign 6:0.8955}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-3225.0535}{txt}{col 53}{lalign 17:Pseudo R2}{col 70} = {res}{ralign 6:0.0000}

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       selfhelp{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimalaidtxt {c |}
{space 3}Treatment 1  {c |}{col 17}{res}{space 2} .1351503{col 29}{space 2} .3677906{col 40}{space 1}    0.37{col 49}{space 3}0.713{col 57}{space 4}-.5861507{col 70}{space 3} .8564513
{txt}{space 3}Treatment 2  {c |}{col 17}{res}{space 2}  .161801{col 29}{space 2} .3704536{col 40}{space 1}    0.44{col 49}{space 3}0.662{col 57}{space 4}-.5647227{col 70}{space 3} .8883247
{txt}{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2} 10.86201{col 29}{space 2} .2782517{col 40}{space 1}   39.04{col 49}{space 3}0.000{col 57}{space 4} 10.31631{col 70}{space 3} 11.40771
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}var(e.selfhelp){c |}{col 17}{res}{space 2} 34.11827{col 29}{space 2} 2.062773{col 57}{space 4} 30.30345{col 70}{space 3} 38.41333
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. tobit giveidp100rnd ib3.humanimalaidtxt, ll ul
{res}{txt}
Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-4441.6765}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log likelihood = {res:-4441.6765}  
Iteration 1:{space 2}Log likelihood = {res:-4409.3954}  
Iteration 2:{space 2}Log likelihood = {res:-4408.8537}  
Iteration 3:{space 2}Log likelihood = {res:-4408.8508}  
Iteration 4:{space 2}Log likelihood = {res:-4408.8508}  
{res}
{txt}{col 1}Tobit regression{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:1,981}
{txt}{col 53}{ralign 17:Uncensored}{col 70} = {res}{ralign 6:1,701}
{txt}{col 1}Limits: Lower = {ralign 2:{res:0}}{col 53}{ralign 17:Left-censored}{col 70} = {res}{ralign 6:109}
{txt}{col 1}        Upper = {ralign 2:{res:10}}{col 53}{ralign 17:   Right-censored}{col 70} = {res}{ralign 6:171}

{txt}{col 53}{lalign 17:LR chi2({res:2})}{col 70} = {res}{ralign 6:6.65}
{txt}{col 53}{lalign 17:Prob > chi2}{col 70} = {res}{ralign 6:0.0359}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-4408.8508}{txt}{col 53}{lalign 17:Pseudo R2}{col 70} = {res}{ralign 6:0.0008}

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       giveidp100rnd{col 22}{c |} Coefficient{col 34}  Std. err.{col 46}      t{col 54}   P>|t|{col 62}     [95% con{col 75}f. interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}humanimalaidtxt {c |}
{space 8}Treatment 1  {c |}{col 22}{res}{space 2}-.0839298{col 34}{space 2} .1433774{col 45}{space 1}   -0.59{col 54}{space 3}0.558{col 62}{space 4}-.3651163{col 75}{space 3} .1972567
{txt}{space 8}Treatment 2  {c |}{col 22}{res}{space 2}-.3560378{col 34}{space 2} .1442682{col 45}{space 1}   -2.47{col 54}{space 3}0.014{col 62}{space 4}-.6389712{col 75}{space 3}-.0731043
{txt}{space 20} {c |}
{space 15}_cons {c |}{col 22}{res}{space 2} 6.302853{col 34}{space 2} .1019083{col 45}{space 1}   61.85{col 54}{space 3}0.000{col 62}{space 4} 6.102994{col 75}{space 3} 6.502712
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}var(e.giveidp100rnd){c |}{col 22}{res}{space 2} 6.694793{col 34}{space 2} .2409685{col 62}{space 4} 6.238508{col 75}{space 3}  7.18445
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Experiment 2 – Extended Controls (Tobit Regression)
. 
. tobit revaidmediatorcheck  ib3.humanimalaidtxt i.ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4 , ll ul
{res}{txt}
Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-4110.6843}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log likelihood = {res:-4110.6843}  
Iteration 1:{space 2}Log likelihood = {res:-4076.4084}  
Iteration 2:{space 2}Log likelihood = {res:-4075.7673}  
Iteration 3:{space 2}Log likelihood = {res: -4075.765}  
Iteration 4:{space 2}Log likelihood = {res: -4075.765}  
{res}
{txt}{col 1}Tobit regression{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:1,894}
{txt}{col 53}{ralign 17:Uncensored}{col 70} = {res}{ralign 6:1,620}
{txt}{col 1}Limits: Lower = {ralign 2:{res:0}}{col 53}{ralign 17:Left-censored}{col 70} = {res}{ralign 6:65}
{txt}{col 1}        Upper = {ralign 2:{res:10}}{col 53}{ralign 17:   Right-censored}{col 70} = {res}{ralign 6:209}

{txt}{col 53}{lalign 17:LR chi2({res:30})}{col 70} = {res}{ralign 6:105.78}
{txt}{col 53}{lalign 17:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 9:-4075.765}{txt}{col 53}{lalign 17:Pseudo R2}{col 70} = {res}{ralign 6:0.0128}

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                    revaidmediatorcheck{col 41}{c |} Coefficient{col 53}  Std. err.{col 65}      t{col 73}   P>|t|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}humanimalaidtxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2} .0311073{col 53}{space 2} .1361476{col 64}{space 1}    0.23{col 73}{space 3}0.819{col 81}{space 4}-.2359105{col 94}{space 3} .2981252
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2}-.1537562{col 53}{space 2} .1365786{col 64}{space 1}   -1.13{col 73}{space 3}0.260{col 81}{space 4}-.4216193{col 94}{space 3} .1141069
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}-.7283965{col 53}{space 2} .1288558{col 64}{space 1}   -5.65{col 73}{space 3}0.000{col 81}{space 4}-.9811132{col 94}{space 3}-.4756797
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2} .3452465{col 53}{space 2} .4019727{col 64}{space 1}    0.86{col 73}{space 3}0.391{col 81}{space 4}-.4431175{col 94}{space 3}  1.13361
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2}-.7507028{col 53}{space 2} .1874016{col 64}{space 1}   -4.01{col 73}{space 3}0.000{col 81}{space 4}-1.118242{col 94}{space 3}-.3831638
{txt}{space 39} {c |}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2}-.0030953{col 53}{space 2} .1494587{col 64}{space 1}   -0.02{col 73}{space 3}0.983{col 81}{space 4}-.2962194{col 94}{space 3} .2900288
{txt}{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2}-.2719448{col 53}{space 2} .2025379{col 64}{space 1}   -1.34{col 73}{space 3}0.180{col 81}{space 4}-.6691698{col 94}{space 3} .1252802
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2}-.0912641{col 53}{space 2} .1131679{col 64}{space 1}   -0.81{col 73}{space 3}0.420{col 81}{space 4}-.3132132{col 94}{space 3} .1306849
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2}-.1271936{col 53}{space 2} .1624053{col 64}{space 1}   -0.78{col 73}{space 3}0.434{col 81}{space 4}-.4457088{col 94}{space 3} .1913217
{txt}{space 30}displaced {c |}{col 41}{res}{space 2} .0888645{col 53}{space 2} .1778204{col 64}{space 1}    0.50{col 73}{space 3}0.617{col 81}{space 4}-.2598835{col 94}{space 3} .4376125
{txt}{space 30}ukrainian {c |}{col 41}{res}{space 2} .1271119{col 53}{space 2} .2969378{col 64}{space 1}    0.43{col 73}{space 3}0.669{col 81}{space 4}-.4552536{col 94}{space 3} .7094774
{txt}{space 32}russian {c |}{col 41}{res}{space 2} -.226246{col 53}{space 2} .4456735{col 64}{space 1}   -0.51{col 73}{space 3}0.612{col 81}{space 4}-1.100318{col 94}{space 3} .6478256
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2} .1680821{col 53}{space 2} .1899471{col 64}{space 1}    0.88{col 73}{space 3}0.376{col 81}{space 4}-.2044493{col 94}{space 3} .5406136
{txt}{space 33}female {c |}{col 41}{res}{space 2}-.5305557{col 53}{space 2} .1204344{col 64}{space 1}   -4.41{col 73}{space 3}0.000{col 81}{space 4}-.7667563{col 94}{space 3}-.2943552
{txt}{space 36}age {c |}{col 41}{res}{space 2}-.0024262{col 53}{space 2} .0050334{col 64}{space 1}   -0.48{col 73}{space 3}0.630{col 81}{space 4}-.0122979{col 94}{space 3} .0074456
{txt}{space 30}education {c |}{col 41}{res}{space 2} .1284344{col 53}{space 2} .0434987{col 64}{space 1}    2.95{col 73}{space 3}0.003{col 81}{space 4} .0431232{col 94}{space 3} .2137456
{txt}{space 33}income {c |}{col 41}{res}{space 2} .0503472{col 53}{space 2} .0699317{col 64}{space 1}    0.72{col 73}{space 3}0.472{col 81}{space 4}-.0868055{col 94}{space 3} .1874999
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2} -.115506{col 53}{space 2} .2662987{col 64}{space 1}   -0.43{col 73}{space 3}0.665{col 81}{space 4} -.637781{col 94}{space 3}  .406769
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2} .1798902{col 53}{space 2} .2146621{col 64}{space 1}    0.84{col 73}{space 3}0.402{col 81}{space 4}-.2411133{col 94}{space 3} .6008936
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2} .5943341{col 53}{space 2} .3105844{col 64}{space 1}    1.91{col 73}{space 3}0.056{col 81}{space 4}-.0147958{col 94}{space 3} 1.203464
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2} .3429735{col 53}{space 2} .2840759{col 64}{space 1}    1.21{col 73}{space 3}0.227{col 81}{space 4}-.2141667{col 94}{space 3} .9001138
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2} .4810321{col 53}{space 2} .4461693{col 64}{space 1}    1.08{col 73}{space 3}0.281{col 81}{space 4}-.3940119{col 94}{space 3} 1.356076
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2} .0540541{col 53}{space 2} .2700379{col 64}{space 1}    0.20{col 73}{space 3}0.841{col 81}{space 4}-.4755545{col 94}{space 3} .5836626
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2} .0893333{col 53}{space 2} .2180969{col 64}{space 1}    0.41{col 73}{space 3}0.682{col 81}{space 4}-.3384065{col 94}{space 3}  .517073
{txt}{space 31}Student  {c |}{col 41}{res}{space 2}-.0022602{col 53}{space 2} .5119651{col 64}{space 1}   -0.00{col 73}{space 3}0.996{col 81}{space 4}-1.006345{col 94}{space 3} 1.001825
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2} .0901618{col 53}{space 2} .3488424{col 64}{space 1}    0.26{col 73}{space 3}0.796{col 81}{space 4} -.594001{col 94}{space 3} .7743246
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2} .3033251{col 53}{space 2} .1624406{col 64}{space 1}    1.87{col 73}{space 3}0.062{col 81}{space 4}-.0152595{col 94}{space 3} .6219097
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2} .2173532{col 53}{space 2} .2183545{col 64}{space 1}    1.00{col 73}{space 3}0.320{col 81}{space 4}-.2108919{col 94}{space 3} .6455983
{txt}{space 31}Central  {c |}{col 41}{res}{space 2} .0131934{col 53}{space 2} .1896662{col 64}{space 1}    0.07{col 73}{space 3}0.945{col 81}{space 4} -.358787{col 94}{space 3} .3851738
{txt}{space 33}South  {c |}{col 41}{res}{space 2} .0940136{col 53}{space 2} .1950129{col 64}{space 1}    0.48{col 73}{space 3}0.630{col 81}{space 4}-.2884531{col 94}{space 3} .4764803
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2} 4.973341{col 53}{space 2} .6570955{col 64}{space 1}    7.57{col 73}{space 3}0.000{col 81}{space 4} 3.684621{col 94}{space 3} 6.262061
{txt}{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}var(e.revaidmediatorcheck){c |}{col 41}{res}{space 2} 5.670851{col 53}{space 2}  .209823{col 81}{space 4} 5.273914{col 94}{space 3} 6.097662
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. tobit providemoral ib3.humanimalaidtxt i.ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4 , ll ul 
{res}{txt}
Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-4446.4394}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log likelihood = {res:-4446.4394}  
Iteration 1:{space 2}Log likelihood = {res:-4256.0225}  
Iteration 2:{space 2}Log likelihood = {res:-4252.0327}  
Iteration 3:{space 2}Log likelihood = {res:-4252.0139}  
Iteration 4:{space 2}Log likelihood = {res:-4252.0139}  
{res}
{txt}{col 1}Tobit regression{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:1,875}
{txt}{col 53}{ralign 17:Uncensored}{col 70} = {res}{ralign 6:1,216}
{txt}{col 1}Limits: Lower = {ralign 2:{res:0}}{col 53}{ralign 17:Left-censored}{col 70} = {res}{ralign 6:257}
{txt}{col 1}        Upper = {ralign 2:{res:10}}{col 53}{ralign 17:   Right-censored}{col 70} = {res}{ralign 6:402}

{txt}{col 53}{lalign 17:LR chi2({res:30})}{col 70} = {res}{ralign 6:101.56}
{txt}{col 53}{lalign 17:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-4252.0139}{txt}{col 53}{lalign 17:Pseudo R2}{col 70} = {res}{ralign 6:0.0118}

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                           providemoral{col 41}{c |} Coefficient{col 53}  Std. err.{col 65}      t{col 73}   P>|t|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}humanimalaidtxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2}-.0951207{col 53}{space 2} .2629906{col 64}{space 1}   -0.36{col 73}{space 3}0.718{col 81}{space 4}-.6109112{col 94}{space 3} .4206699
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2} -.213061{col 53}{space 2} .2645474{col 64}{space 1}   -0.81{col 73}{space 3}0.421{col 81}{space 4}-.7319047{col 94}{space 3} .3057827
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}-1.197328{col 53}{space 2} .2493668{col 64}{space 1}   -4.80{col 73}{space 3}0.000{col 81}{space 4}-1.686399{col 94}{space 3}-.7082572
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2} 1.023046{col 53}{space 2} .8154855{col 64}{space 1}    1.25{col 73}{space 3}0.210{col 81}{space 4}-.5763249{col 94}{space 3} 2.622418
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2}-.7972641{col 53}{space 2} .3617939{col 64}{space 1}   -2.20{col 73}{space 3}0.028{col 81}{space 4}-1.506833{col 94}{space 3}-.0876957
{txt}{space 39} {c |}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2} .2759391{col 53}{space 2} .2898091{col 64}{space 1}    0.95{col 73}{space 3}0.341{col 81}{space 4}-.2924491{col 94}{space 3} .8443273
{txt}{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2} .2300152{col 53}{space 2}  .389579{col 64}{space 1}    0.59{col 73}{space 3}0.555{col 81}{space 4}-.5340469{col 94}{space 3} .9940772
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2}-.1532841{col 53}{space 2} .2190689{col 64}{space 1}   -0.70{col 73}{space 3}0.484{col 81}{space 4}-.5829331{col 94}{space 3} .2763649
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2} .3045968{col 53}{space 2} .3152537{col 64}{space 1}    0.97{col 73}{space 3}0.334{col 81}{space 4}-.3136948{col 94}{space 3} .9228883
{txt}{space 30}displaced {c |}{col 41}{res}{space 2} .0202834{col 53}{space 2}  .344515{col 64}{space 1}    0.06{col 73}{space 3}0.953{col 81}{space 4}-.6553968{col 94}{space 3} .6959637
{txt}{space 30}ukrainian {c |}{col 41}{res}{space 2}-.1571388{col 53}{space 2} .5776434{col 64}{space 1}   -0.27{col 73}{space 3}0.786{col 81}{space 4}-1.290042{col 94}{space 3} .9757647
{txt}{space 32}russian {c |}{col 41}{res}{space 2} .5540225{col 53}{space 2} .8751666{col 64}{space 1}    0.63{col 73}{space 3}0.527{col 81}{space 4}-1.162398{col 94}{space 3} 2.270443
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2}-.0577466{col 53}{space 2}  .369404{col 64}{space 1}   -0.16{col 73}{space 3}0.876{col 81}{space 4}-.7822404{col 94}{space 3} .6667473
{txt}{space 33}female {c |}{col 41}{res}{space 2}-1.230691{col 53}{space 2} .2334624{col 64}{space 1}   -5.27{col 73}{space 3}0.000{col 81}{space 4}-1.688569{col 94}{space 3}-.7728125
{txt}{space 36}age {c |}{col 41}{res}{space 2} .0249295{col 53}{space 2} .0097312{col 64}{space 1}    2.56{col 73}{space 3}0.010{col 81}{space 4} .0058442{col 94}{space 3} .0440149
{txt}{space 30}education {c |}{col 41}{res}{space 2} .0591213{col 53}{space 2} .0844235{col 64}{space 1}    0.70{col 73}{space 3}0.484{col 81}{space 4}-.1064543{col 94}{space 3} .2246969
{txt}{space 33}income {c |}{col 41}{res}{space 2}-.1006839{col 53}{space 2} .1362774{col 64}{space 1}   -0.74{col 73}{space 3}0.460{col 81}{space 4} -.367958{col 94}{space 3} .1665902
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2}-.3750923{col 53}{space 2} .5189002{col 64}{space 1}   -0.72{col 73}{space 3}0.470{col 81}{space 4}-1.392786{col 94}{space 3} .6426011
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2}-.3671716{col 53}{space 2} .4149317{col 64}{space 1}   -0.88{col 73}{space 3}0.376{col 81}{space 4}-1.180957{col 94}{space 3} .4466135
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2} .9630862{col 53}{space 2} .5965634{col 64}{space 1}    1.61{col 73}{space 3}0.107{col 81}{space 4}-.2069242{col 94}{space 3} 2.133097
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2}-.0226607{col 53}{space 2} .5475326{col 64}{space 1}   -0.04{col 73}{space 3}0.967{col 81}{space 4}-1.096509{col 94}{space 3} 1.051188
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2}  .548945{col 53}{space 2} .8624613{col 64}{space 1}    0.64{col 73}{space 3}0.525{col 81}{space 4}-1.142558{col 94}{space 3} 2.240448
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2}-.4242791{col 53}{space 2} .5258934{col 64}{space 1}   -0.81{col 73}{space 3}0.420{col 81}{space 4}-1.455688{col 94}{space 3} .6071297
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2} .0267104{col 53}{space 2}   .42308{col 64}{space 1}    0.06{col 73}{space 3}0.950{col 81}{space 4}-.8030555{col 94}{space 3} .8564762
{txt}{space 31}Student  {c |}{col 41}{res}{space 2} .9257764{col 53}{space 2} .9874704{col 64}{space 1}    0.94{col 73}{space 3}0.349{col 81}{space 4}  -1.0109{col 94}{space 3} 2.862453
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2} .8251418{col 53}{space 2} .6693304{col 64}{space 1}    1.23{col 73}{space 3}0.218{col 81}{space 4}-.4875829{col 94}{space 3} 2.137866
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2} .5401475{col 53}{space 2} .3144371{col 64}{space 1}    1.72{col 73}{space 3}0.086{col 81}{space 4}-.0765425{col 94}{space 3} 1.156837
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2}-.0201852{col 53}{space 2} .4229169{col 64}{space 1}   -0.05{col 73}{space 3}0.962{col 81}{space 4}-.8496313{col 94}{space 3} .8092609
{txt}{space 31}Central  {c |}{col 41}{res}{space 2}-.1718912{col 53}{space 2} .3678574{col 64}{space 1}   -0.47{col 73}{space 3}0.640{col 81}{space 4}-.8933517{col 94}{space 3} .5495693
{txt}{space 33}South  {c |}{col 41}{res}{space 2}-.2004177{col 53}{space 2}  .379048{col 64}{space 1}   -0.53{col 73}{space 3}0.597{col 81}{space 4}-.9438258{col 94}{space 3} .5429905
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2} 5.456678{col 53}{space 2} 1.278395{col 64}{space 1}    4.27{col 73}{space 3}0.000{col 81}{space 4} 2.949426{col 94}{space 3}  7.96393
{txt}{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}var(e.providemoral){c |}{col 41}{res}{space 2} 19.87294{col 53}{space 2}  .903654{col 81}{space 4} 18.17738{col 94}{space 3} 21.72666
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. tobit selfhelp ib3.humanimalaidtxt i.ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4 , ll ul 
{res}{txt}
Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-3746.2272}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log likelihood = {res:-3746.2272}  
Iteration 1:{space 2}Log likelihood = {res:-3222.5678}  
Iteration 2:{space 2}Log likelihood = {res:-3101.4885}  
Iteration 3:{space 2}Log likelihood = {res: -3088.747}  
Iteration 4:{space 2}Log likelihood = {res:-3088.5034}  
Iteration 5:{space 2}Log likelihood = {res:-3088.5031}  
Iteration 6:{space 2}Log likelihood = {res:-3088.5031}  
{res}
{txt}{col 1}Tobit regression{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:1,892}
{txt}{col 53}{ralign 17:Uncensored}{col 70} = {res}{ralign 6:734}
{txt}{col 1}Limits: Lower = {ralign 2:{res:0}}{col 53}{ralign 17:Left-censored}{col 70} = {res}{ralign 6:62}
{txt}{col 1}        Upper = {ralign 2:{res:10}}{col 53}{ralign 17:   Right-censored}{col 70} = {res}{ralign 6:1,096}

{txt}{col 53}{lalign 17:LR chi2({res:30})}{col 70} = {res}{ralign 6:34.45}
{txt}{col 53}{lalign 17:Prob > chi2}{col 70} = {res}{ralign 6:0.2633}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-3088.5031}{txt}{col 53}{lalign 17:Pseudo R2}{col 70} = {res}{ralign 6:0.0055}

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                               selfhelp{col 41}{c |} Coefficient{col 53}  Std. err.{col 65}      t{col 73}   P>|t|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}humanimalaidtxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2} .0384468{col 53}{space 2} .3725583{col 64}{space 1}    0.10{col 73}{space 3}0.918{col 81}{space 4}-.6922289{col 94}{space 3} .7691226
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2} .1259539{col 53}{space 2} .3742895{col 64}{space 1}    0.34{col 73}{space 3}0.737{col 81}{space 4}-.6081172{col 94}{space 3} .8600251
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2} .9086757{col 53}{space 2} .3501655{col 64}{space 1}    2.59{col 73}{space 3}0.010{col 81}{space 4} .2219174{col 94}{space 3} 1.595434
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2} 1.706084{col 53}{space 2} 1.148243{col 64}{space 1}    1.49{col 73}{space 3}0.137{col 81}{space 4}-.5458935{col 94}{space 3} 3.958062
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2} .5644838{col 53}{space 2} .5089838{col 64}{space 1}    1.11{col 73}{space 3}0.268{col 81}{space 4} -.433755{col 94}{space 3} 1.562723
{txt}{space 39} {c |}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2}-.5327685{col 53}{space 2} .4021877{col 64}{space 1}   -1.32{col 73}{space 3}0.185{col 81}{space 4}-1.321555{col 94}{space 3} .2560177
{txt}{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2}-.1706226{col 53}{space 2}  .551116{col 64}{space 1}   -0.31{col 73}{space 3}0.757{col 81}{space 4}-1.251493{col 94}{space 3} .9102475
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2}-.0778753{col 53}{space 2} .3103837{col 64}{space 1}   -0.25{col 73}{space 3}0.802{col 81}{space 4}-.6866118{col 94}{space 3} .5308612
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2} .0159674{col 53}{space 2} .4448406{col 64}{space 1}    0.04{col 73}{space 3}0.971{col 81}{space 4}-.8564713{col 94}{space 3} .8884062
{txt}{space 30}displaced {c |}{col 41}{res}{space 2} .3724989{col 53}{space 2} .4878027{col 64}{space 1}    0.76{col 73}{space 3}0.445{col 81}{space 4}-.5841987{col 94}{space 3} 1.329196
{txt}{space 30}ukrainian {c |}{col 41}{res}{space 2} 1.836392{col 53}{space 2}  .802966{col 64}{space 1}    2.29{col 73}{space 3}0.022{col 81}{space 4} .2615841{col 94}{space 3}   3.4112
{txt}{space 32}russian {c |}{col 41}{res}{space 2} .4931463{col 53}{space 2} 1.205089{col 64}{space 1}    0.41{col 73}{space 3}0.682{col 81}{space 4} -1.87032{col 94}{space 3} 2.856613
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2} .3015641{col 53}{space 2} .5261118{col 64}{space 1}    0.57{col 73}{space 3}0.567{col 81}{space 4}-.7302668{col 94}{space 3} 1.333395
{txt}{space 33}female {c |}{col 41}{res}{space 2} .4456361{col 53}{space 2} .3296256{col 64}{space 1}    1.35{col 73}{space 3}0.177{col 81}{space 4}-.2008385{col 94}{space 3} 1.092111
{txt}{space 36}age {c |}{col 41}{res}{space 2} .0197691{col 53}{space 2} .0138529{col 64}{space 1}    1.43{col 73}{space 3}0.154{col 81}{space 4}-.0073998{col 94}{space 3} .0469379
{txt}{space 30}education {c |}{col 41}{res}{space 2}-.0795112{col 53}{space 2} .1194887{col 64}{space 1}   -0.67{col 73}{space 3}0.506{col 81}{space 4}-.3138571{col 94}{space 3} .1548347
{txt}{space 33}income {c |}{col 41}{res}{space 2} .2213127{col 53}{space 2} .1936989{col 64}{space 1}    1.14{col 73}{space 3}0.253{col 81}{space 4}-.1585771{col 94}{space 3} .6012025
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2} .4708089{col 53}{space 2}  .735594{col 64}{space 1}    0.64{col 73}{space 3}0.522{col 81}{space 4}-.9718667{col 94}{space 3} 1.913484
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2} .2650131{col 53}{space 2} .5852984{col 64}{space 1}    0.45{col 73}{space 3}0.651{col 81}{space 4}-.8828969{col 94}{space 3} 1.412923
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2} .0964744{col 53}{space 2} .8419777{col 64}{space 1}    0.11{col 73}{space 3}0.909{col 81}{space 4}-1.554845{col 94}{space 3} 1.747794
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2}-.3648056{col 53}{space 2} .7679338{col 64}{space 1}   -0.48{col 73}{space 3}0.635{col 81}{space 4}-1.870907{col 94}{space 3} 1.141296
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2} 1.035532{col 53}{space 2} 1.220195{col 64}{space 1}    0.85{col 73}{space 3}0.396{col 81}{space 4}-1.357561{col 94}{space 3} 3.428625
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2}-.0854013{col 53}{space 2} .7399093{col 64}{space 1}   -0.12{col 73}{space 3}0.908{col 81}{space 4} -1.53654{col 94}{space 3} 1.365738
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2}  .251172{col 53}{space 2} .6017329{col 64}{space 1}    0.42{col 73}{space 3}0.676{col 81}{space 4}  -.92897{col 94}{space 3} 1.431314
{txt}{space 31}Student  {c |}{col 41}{res}{space 2}-1.021172{col 53}{space 2} 1.329767{col 64}{space 1}   -0.77{col 73}{space 3}0.443{col 81}{space 4}-3.629163{col 94}{space 3}  1.58682
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2}-.7792661{col 53}{space 2} .9303482{col 64}{space 1}   -0.84{col 73}{space 3}0.402{col 81}{space 4}-2.603901{col 94}{space 3} 1.045369
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2}-.4690015{col 53}{space 2} .4468705{col 64}{space 1}   -1.05{col 73}{space 3}0.294{col 81}{space 4}-1.345421{col 94}{space 3} .4074183
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2} .2421129{col 53}{space 2} .5916069{col 64}{space 1}    0.41{col 73}{space 3}0.682{col 81}{space 4}-.9181695{col 94}{space 3} 1.402395
{txt}{space 31}Central  {c |}{col 41}{res}{space 2} .5601621{col 53}{space 2} .5151328{col 64}{space 1}    1.09{col 73}{space 3}0.277{col 81}{space 4}-.4501363{col 94}{space 3} 1.570461
{txt}{space 33}South  {c |}{col 41}{res}{space 2}  .826935{col 53}{space 2} .5324611{col 64}{space 1}    1.55{col 73}{space 3}0.121{col 81}{space 4}-.2173483{col 94}{space 3} 1.871218
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2}  6.82886{col 53}{space 2} 1.802031{col 64}{space 1}    3.79{col 73}{space 3}0.000{col 81}{space 4} 3.294647{col 94}{space 3} 10.36307
{txt}{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 25}var(e.selfhelp){c |}{col 41}{res}{space 2} 32.96238{col 53}{space 2} 2.025757{col 81}{space 4} 29.21948{col 94}{space 3} 37.18472
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. tobit giveidp100rnd ib3.humanimalaidtxt i.ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4 , ll ul 
{res}{txt}
Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-4152.7497}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log likelihood = {res:-4152.7497}  
Iteration 1:{space 2}Log likelihood = {res:-4122.0791}  
Iteration 2:{space 2}Log likelihood = {res:-4121.5611}  
Iteration 3:{space 2}Log likelihood = {res:-4121.5599}  
Iteration 4:{space 2}Log likelihood = {res:-4121.5599}  
{res}
{txt}{col 1}Tobit regression{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:1,906}
{txt}{col 53}{ralign 17:Uncensored}{col 70} = {res}{ralign 6:1,643}
{txt}{col 1}Limits: Lower = {ralign 2:{res:0}}{col 53}{ralign 17:Left-censored}{col 70} = {res}{ralign 6:102}
{txt}{col 1}        Upper = {ralign 2:{res:10}}{col 53}{ralign 17:   Right-censored}{col 70} = {res}{ralign 6:161}

{txt}{col 53}{lalign 17:LR chi2({res:30})}{col 70} = {res}{ralign 6:228.06}
{txt}{col 53}{lalign 17:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-4121.5599}{txt}{col 53}{lalign 17:Pseudo R2}{col 70} = {res}{ralign 6:0.0269}

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                          giveidp100rnd{col 41}{c |} Coefficient{col 53}  Std. err.{col 65}      t{col 73}   P>|t|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}humanimalaidtxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2}-.0764984{col 53}{space 2} .1369138{col 64}{space 1}   -0.56{col 73}{space 3}0.576{col 81}{space 4}-.3450178{col 94}{space 3} .1920211
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2}-.3409791{col 53}{space 2} .1377851{col 64}{space 1}   -2.47{col 73}{space 3}0.013{col 81}{space 4}-.6112072{col 94}{space 3} -.070751
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}-.8042502{col 53}{space 2} .1297104{col 64}{space 1}   -6.20{col 73}{space 3}0.000{col 81}{space 4}-1.058642{col 94}{space 3}-.5498583
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2}   .18758{col 53}{space 2} .4128271{col 64}{space 1}    0.45{col 73}{space 3}0.650{col 81}{space 4}-.6220687{col 94}{space 3} .9972286
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2} -.809198{col 53}{space 2} .1892381{col 64}{space 1}   -4.28{col 73}{space 3}0.000{col 81}{space 4}-1.180337{col 94}{space 3}-.4380587
{txt}{space 39} {c |}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2} .0404197{col 53}{space 2} .1514463{col 64}{space 1}    0.27{col 73}{space 3}0.790{col 81}{space 4}-.2566012{col 94}{space 3} .3374405
{txt}{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2}-.4803112{col 53}{space 2}  .204285{col 64}{space 1}   -2.35{col 73}{space 3}0.019{col 81}{space 4} -.880961{col 94}{space 3}-.0796614
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2}-.0856961{col 53}{space 2} .1141139{col 64}{space 1}   -0.75{col 73}{space 3}0.453{col 81}{space 4}-.3094996{col 94}{space 3} .1381075
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2} .1160614{col 53}{space 2} .1649998{col 64}{space 1}    0.70{col 73}{space 3}0.482{col 81}{space 4}-.2075411{col 94}{space 3} .4396638
{txt}{space 30}displaced {c |}{col 41}{res}{space 2}  .095478{col 53}{space 2} .1799048{col 64}{space 1}    0.53{col 73}{space 3}0.596{col 81}{space 4}-.2573566{col 94}{space 3} .4483126
{txt}{space 30}ukrainian {c |}{col 41}{res}{space 2} .0934823{col 53}{space 2} .2952739{col 64}{space 1}    0.32{col 73}{space 3}0.752{col 81}{space 4}-.4856176{col 94}{space 3} .6725822
{txt}{space 32}russian {c |}{col 41}{res}{space 2}-.3048914{col 53}{space 2} .4477897{col 64}{space 1}   -0.68{col 73}{space 3}0.496{col 81}{space 4} -1.18311{col 94}{space 3} .5733269
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2} .2305569{col 53}{space 2} .1919563{col 64}{space 1}    1.20{col 73}{space 3}0.230{col 81}{space 4}-.1459134{col 94}{space 3} .6070271
{txt}{space 33}female {c |}{col 41}{res}{space 2}-1.335141{col 53}{space 2} .1216129{col 64}{space 1}  -10.98{col 73}{space 3}0.000{col 81}{space 4}-1.573652{col 94}{space 3}-1.096631
{txt}{space 36}age {c |}{col 41}{res}{space 2}  .008535{col 53}{space 2} .0051161{col 64}{space 1}    1.67{col 73}{space 3}0.095{col 81}{space 4}-.0014988{col 94}{space 3} .0185689
{txt}{space 30}education {c |}{col 41}{res}{space 2} .0796587{col 53}{space 2} .0439485{col 64}{space 1}    1.81{col 73}{space 3}0.070{col 81}{space 4}-.0065343{col 94}{space 3} .1658518
{txt}{space 33}income {c |}{col 41}{res}{space 2} .0130791{col 53}{space 2} .0705654{col 64}{space 1}    0.19{col 73}{space 3}0.853{col 81}{space 4}-.1253158{col 94}{space 3}  .151474
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2} .3958391{col 53}{space 2} .2704248{col 64}{space 1}    1.46{col 73}{space 3}0.143{col 81}{space 4}-.1345259{col 94}{space 3}  .926204
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2} .3576444{col 53}{space 2} .2166518{col 64}{space 1}    1.65{col 73}{space 3}0.099{col 81}{space 4}-.0672595{col 94}{space 3} .7825483
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2} .9564322{col 53}{space 2} .3140175{col 64}{space 1}    3.05{col 73}{space 3}0.002{col 81}{space 4} .3405718{col 94}{space 3} 1.572292
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2} .5352979{col 53}{space 2} .2846027{col 64}{space 1}    1.88{col 73}{space 3}0.060{col 81}{space 4}-.0228733{col 94}{space 3} 1.093469
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2} .0745144{col 53}{space 2} .4466487{col 64}{space 1}    0.17{col 73}{space 3}0.868{col 81}{space 4}-.8014661{col 94}{space 3} .9504949
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2} .2400024{col 53}{space 2}  .273477{col 64}{space 1}    0.88{col 73}{space 3}0.380{col 81}{space 4}-.2963488{col 94}{space 3} .7763536
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2} .1432777{col 53}{space 2} .2209537{col 64}{space 1}    0.65{col 73}{space 3}0.517{col 81}{space 4}-.2900632{col 94}{space 3} .5766187
{txt}{space 31}Student  {c |}{col 41}{res}{space 2} .3046648{col 53}{space 2} .5163402{col 64}{space 1}    0.59{col 73}{space 3}0.555{col 81}{space 4}-.7079967{col 94}{space 3} 1.317326
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2} .4633856{col 53}{space 2} .3523306{col 64}{space 1}    1.32{col 73}{space 3}0.189{col 81}{space 4}-.2276155{col 94}{space 3} 1.154387
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2} .1945893{col 53}{space 2} .1638586{col 64}{space 1}    1.19{col 73}{space 3}0.235{col 81}{space 4}-.1267749{col 94}{space 3} .5159535
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2} .4672304{col 53}{space 2} .2201947{col 64}{space 1}    2.12{col 73}{space 3}0.034{col 81}{space 4} .0353782{col 94}{space 3} .8990826
{txt}{space 31}Central  {c |}{col 41}{res}{space 2} .1043294{col 53}{space 2} .1913659{col 64}{space 1}    0.55{col 73}{space 3}0.586{col 81}{space 4} -.270983{col 94}{space 3} .4796418
{txt}{space 33}South  {c |}{col 41}{res}{space 2} .3002692{col 53}{space 2} .1971809{col 64}{space 1}    1.52{col 73}{space 3}0.128{col 81}{space 4}-.0864477{col 94}{space 3} .6869862
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2} 5.705785{col 53}{space 2} .6664758{col 64}{space 1}    8.56{col 73}{space 3}0.000{col 81}{space 4} 4.398673{col 94}{space 3} 7.012897
{txt}{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}var(e.giveidp100rnd){c |}{col 41}{res}{space 2}  5.79363{col 53}{space 2}  .211679{col 81}{space 4} 5.393004{col 94}{space 3} 6.224017
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Experiment 2 – Human versus Animal Resource Allocation (Ordered Probit Regression)
. 
. oprobit revaidmediatorcheck ib3.humanimalaidtxt, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-2893.8708}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-2893.1888}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-2893.1888}  
{res}
{txt}{col 1}Ordered probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,971}
{txt}{col 57}{lalign 13:Wald chi2({res:2})}{col 70} = {res}{ralign 6:1.36}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.5055}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-2893.1888}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0002}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}revaidmediatorcheck{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      z{col 53}   P>|z|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}humanimalaidtxt {c |}
{space 7}Treatment 1  {c |}{col 21}{res}{space 2}-.0359858{col 33}{space 2} .0611728{col 44}{space 1}   -0.59{col 53}{space 3}0.556{col 61}{space 4}-.1558823{col 74}{space 3} .0839106
{txt}{space 7}Treatment 2  {c |}{col 21}{res}{space 2}-.0709435{col 33}{space 2} .0607427{col 44}{space 1}   -1.17{col 53}{space 3}0.243{col 61}{space 4} -.189997{col 74}{space 3} .0481099
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}/cut1 {c |}{col 21}{res}{space 2}-1.841259{col 33}{space 2} .0645948{col 61}{space 4}-1.967863{col 74}{space 3}-1.714656
{txt}{space 14}/cut2 {c |}{col 21}{res}{space 2}-1.791624{col 33}{space 2} .0634703{col 61}{space 4}-1.916024{col 74}{space 3}-1.667225
{txt}{space 14}/cut3 {c |}{col 21}{res}{space 2}-1.613607{col 33}{space 2} .0592031{col 61}{space 4}-1.729643{col 74}{space 3}-1.497571
{txt}{space 14}/cut4 {c |}{col 21}{res}{space 2}-1.436696{col 33}{space 2} .0557002{col 61}{space 4}-1.545866{col 74}{space 3}-1.327526
{txt}{space 14}/cut5 {c |}{col 21}{res}{space 2}-1.359562{col 33}{space 2} .0543361{col 61}{space 4}-1.466059{col 74}{space 3}-1.253065
{txt}{space 14}/cut6 {c |}{col 21}{res}{space 2} .4761708{col 33}{space 2} .0464055{col 61}{space 4} .3852178{col 74}{space 3} .5671239
{txt}{space 14}/cut7 {c |}{col 21}{res}{space 2} .6989843{col 33}{space 2} .0472543{col 61}{space 4} .6063675{col 74}{space 3} .7916011
{txt}{space 14}/cut8 {c |}{col 21}{res}{space 2} .9312932{col 33}{space 2} .0487709{col 61}{space 4} .8357041{col 74}{space 3} 1.026882
{txt}{space 14}/cut9 {c |}{col 21}{res}{space 2} 1.094455{col 33}{space 2} .0497357{col 61}{space 4} .9969745{col 74}{space 3} 1.191935
{txt}{space 13}/cut10 {c |}{col 21}{res}{space 2} 1.188496{col 33}{space 2} .0503274{col 61}{space 4} 1.089856{col 74}{space 3} 1.287136
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. oprobit providemoral ib3.humanimalaidtxt, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-3167.4007}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-3167.0085}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-3167.0085}  
{res}
{txt}{col 1}Ordered probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,945}
{txt}{col 57}{lalign 13:Wald chi2({res:2})}{col 70} = {res}{ralign 6:0.79}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.6740}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-3167.0085}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0001}

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}   providemoral{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimalaidtxt {c |}
{space 3}Treatment 1  {c |}{col 17}{res}{space 2}-.0467819{col 29}{space 2} .0593861{col 40}{space 1}   -0.79{col 49}{space 3}0.431{col 57}{space 4}-.1631765{col 70}{space 3} .0696126
{txt}{space 3}Treatment 2  {c |}{col 17}{res}{space 2}-.0447623{col 29}{space 2} .0598377{col 40}{space 1}   -0.75{col 49}{space 3}0.454{col 57}{space 4}-.1620421{col 70}{space 3} .0725175
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}/cut1 {c |}{col 17}{res}{space 2}-1.121153{col 29}{space 2}  .049204{col 57}{space 4}-1.217591{col 70}{space 3}-1.024715
{txt}{space 10}/cut2 {c |}{col 17}{res}{space 2}-1.089007{col 29}{space 2} .0488166{col 57}{space 4}-1.184686{col 70}{space 3} -.993328
{txt}{space 10}/cut3 {c |}{col 17}{res}{space 2}-1.006828{col 29}{space 2} .0483545{col 57}{space 4}-1.101601{col 70}{space 3}-.9120552
{txt}{space 10}/cut4 {c |}{col 17}{res}{space 2}-.9404915{col 29}{space 2} .0480624{col 57}{space 4}-1.034692{col 70}{space 3}-.8462908
{txt}{space 10}/cut5 {c |}{col 17}{res}{space 2}-.8669269{col 29}{space 2} .0475691{col 57}{space 4}-.9601606{col 70}{space 3}-.7736932
{txt}{space 10}/cut6 {c |}{col 17}{res}{space 2} .3894027{col 29}{space 2} .0451428{col 57}{space 4} .3009244{col 70}{space 3}  .477881
{txt}{space 10}/cut7 {c |}{col 17}{res}{space 2} .4565727{col 29}{space 2} .0452218{col 57}{space 4} .3679396{col 70}{space 3} .5452058
{txt}{space 10}/cut8 {c |}{col 17}{res}{space 2} .5609624{col 29}{space 2}   .04556{col 57}{space 4} .4716664{col 70}{space 3} .6502584
{txt}{space 10}/cut9 {c |}{col 17}{res}{space 2} .7191967{col 29}{space 2} .0462183{col 57}{space 4} .6286105{col 70}{space 3} .8097829
{txt}{space 9}/cut10 {c |}{col 17}{res}{space 2} .7537843{col 29}{space 2} .0465199{col 57}{space 4} .6626071{col 70}{space 3} .8449616
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. oprobit selfhelp ib3.humanimalaidtxt, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res: -2642.055}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-2641.9304}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-2641.9304}  
{res}
{txt}{col 1}Ordered probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,965}
{txt}{col 57}{lalign 13:Wald chi2({res:2})}{col 70} = {res}{ralign 6:0.26}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.8802}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-2641.9304}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0000}

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}       selfhelp{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimalaidtxt {c |}
{space 3}Treatment 1  {c |}{col 17}{res}{space 2} .0201709{col 29}{space 2} .0640466{col 40}{space 1}    0.31{col 49}{space 3}0.753{col 57}{space 4} -.105358{col 70}{space 3} .1456999
{txt}{space 3}Treatment 2  {c |}{col 17}{res}{space 2} .0316358{col 29}{space 2}  .063519{col 40}{space 1}    0.50{col 49}{space 3}0.618{col 57}{space 4}-.0928591{col 70}{space 3} .1561308
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}/cut1 {c |}{col 17}{res}{space 2} -1.81339{col 29}{space 2} .0643672{col 57}{space 4}-1.939547{col 70}{space 3}-1.687232
{txt}{space 10}/cut2 {c |}{col 17}{res}{space 2}-1.761433{col 29}{space 2} .0620668{col 57}{space 4}-1.883082{col 70}{space 3}-1.639784
{txt}{space 10}/cut3 {c |}{col 17}{res}{space 2}-1.691497{col 29}{space 2} .0606795{col 57}{space 4}-1.810426{col 70}{space 3}-1.572567
{txt}{space 10}/cut4 {c |}{col 17}{res}{space 2}-1.624097{col 29}{space 2} .0596631{col 57}{space 4}-1.741034{col 70}{space 3}-1.507159
{txt}{space 10}/cut5 {c |}{col 17}{res}{space 2}-1.572366{col 29}{space 2} .0587659{col 57}{space 4}-1.687545{col 70}{space 3}-1.457187
{txt}{space 10}/cut6 {c |}{col 17}{res}{space 2}-.6119616{col 29}{space 2} .0468973{col 57}{space 4}-.7038785{col 70}{space 3}-.5200446
{txt}{space 10}/cut7 {c |}{col 17}{res}{space 2}-.5888091{col 29}{space 2} .0467826{col 57}{space 4}-.6805013{col 70}{space 3}-.4971168
{txt}{space 10}/cut8 {c |}{col 17}{res}{space 2}-.4760276{col 29}{space 2} .0462744{col 57}{space 4}-.5667238{col 70}{space 3}-.3853314
{txt}{space 10}/cut9 {c |}{col 17}{res}{space 2}-.2679308{col 29}{space 2} .0459976{col 57}{space 4}-.3580844{col 70}{space 3}-.1777771
{txt}{space 9}/cut10 {c |}{col 17}{res}{space 2}-.1838039{col 29}{space 2} .0459447{col 57}{space 4}-.2738539{col 70}{space 3}-.0937539
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. oprobit giveidp100rnd ib3.humanimalaidtxt, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-3520.1268}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-3517.1762}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-3517.1762}  
{res}
{txt}{col 1}Ordered probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,981}
{txt}{col 57}{lalign 13:Wald chi2({res:2})}{col 70} = {res}{ralign 6:5.87}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0532}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-3517.1762}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0008}

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}  giveidp100rnd{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimalaidtxt {c |}
{space 3}Treatment 1  {c |}{col 17}{res}{space 2}-.0232354{col 29}{space 2}  .057126{col 40}{space 1}   -0.41{col 49}{space 3}0.684{col 57}{space 4}-.1352003{col 70}{space 3} .0887296
{txt}{space 3}Treatment 2  {c |}{col 17}{res}{space 2}-.1312535{col 29}{space 2} .0578142{col 40}{space 1}   -2.27{col 49}{space 3}0.023{col 57}{space 4}-.2445674{col 70}{space 3}-.0179397
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}/cut1 {c |}{col 17}{res}{space 2}-1.652127{col 29}{space 2} .0564057{col 57}{space 4} -1.76268{col 70}{space 3}-1.541574
{txt}{space 10}/cut2 {c |}{col 17}{res}{space 2}-1.643097{col 29}{space 2} .0562967{col 57}{space 4}-1.753436{col 70}{space 3}-1.532757
{txt}{space 10}/cut3 {c |}{col 17}{res}{space 2}-1.595603{col 29}{space 2} .0553461{col 57}{space 4} -1.70408{col 70}{space 3}-1.487127
{txt}{space 10}/cut4 {c |}{col 17}{res}{space 2}-1.502507{col 29}{space 2} .0535521{col 57}{space 4}-1.607467{col 70}{space 3}-1.397547
{txt}{space 10}/cut5 {c |}{col 17}{res}{space 2}-1.430193{col 29}{space 2} .0518504{col 57}{space 4}-1.531818{col 70}{space 3}-1.328568
{txt}{space 10}/cut6 {c |}{col 17}{res}{space 2}-.0986524{col 29}{space 2} .0435244{col 57}{space 4}-.1839586{col 70}{space 3}-.0133462
{txt}{space 10}/cut7 {c |}{col 17}{res}{space 2} .0841516{col 29}{space 2} .0436288{col 57}{space 4}-.0013593{col 70}{space 3} .1696625
{txt}{space 10}/cut8 {c |}{col 17}{res}{space 2} .5994299{col 29}{space 2} .0452452{col 57}{space 4}  .510751{col 70}{space 3} .6881089
{txt}{space 10}/cut9 {c |}{col 17}{res}{space 2} 1.090164{col 29}{space 2} .0496208{col 57}{space 4} .9929087{col 70}{space 3} 1.187419
{txt}{space 9}/cut10 {c |}{col 17}{res}{space 2}  1.31467{col 29}{space 2} .0526413{col 57}{space 4} 1.211495{col 70}{space 3} 1.417845
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Experiment 2 – Extended Controls (Ordered Probit Regression)
. 
. oprobit revaidmediatorcheck  ib3.humanimalaidtxt i.ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4 , robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-2786.8873}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-2727.2278}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-2727.1284}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-2727.1284}  
{res}
{txt}{col 1}Ordered probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,894}
{txt}{col 57}{lalign 13:Wald chi2({res:30})}{col 70} = {res}{ralign 6:117.44}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-2727.1284}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0214}

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                    revaidmediatorcheck{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      z{col 73}   P>|z|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}humanimalaidtxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2}-.0061147{col 53}{space 2} .0632738{col 64}{space 1}   -0.10{col 73}{space 3}0.923{col 81}{space 4}-.1301291{col 94}{space 3} .1178997
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2}-.0600911{col 53}{space 2} .0627241{col 64}{space 1}   -0.96{col 73}{space 3}0.338{col 81}{space 4} -.183028{col 94}{space 3} .0628458
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}-.3275353{col 53}{space 2} .0613483{col 64}{space 1}   -5.34{col 73}{space 3}0.000{col 81}{space 4}-.4477758{col 94}{space 3}-.2072949
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2} .0994845{col 53}{space 2} .2176765{col 64}{space 1}    0.46{col 73}{space 3}0.648{col 81}{space 4}-.3271536{col 94}{space 3} .5261227
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2}-.3260597{col 53}{space 2} .0806978{col 64}{space 1}   -4.04{col 73}{space 3}0.000{col 81}{space 4}-.4842245{col 94}{space 3}-.1678949
{txt}{space 39} {c |}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2}-.0187533{col 53}{space 2} .0707753{col 64}{space 1}   -0.26{col 73}{space 3}0.791{col 81}{space 4}-.1574704{col 94}{space 3} .1199638
{txt}{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2}-.0789664{col 53}{space 2} .0929745{col 64}{space 1}   -0.85{col 73}{space 3}0.396{col 81}{space 4} -.261193{col 94}{space 3} .1032602
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2}-.0258332{col 53}{space 2} .0517113{col 64}{space 1}   -0.50{col 73}{space 3}0.617{col 81}{space 4}-.1271855{col 94}{space 3} .0755192
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2}-.0466999{col 53}{space 2} .0769775{col 64}{space 1}   -0.61{col 73}{space 3}0.544{col 81}{space 4} -.197573{col 94}{space 3} .1041731
{txt}{space 30}displaced {c |}{col 41}{res}{space 2} .0441235{col 53}{space 2} .0782173{col 64}{space 1}    0.56{col 73}{space 3}0.573{col 81}{space 4}-.1091797{col 94}{space 3} .1974266
{txt}{space 30}ukrainian {c |}{col 41}{res}{space 2} .0952862{col 53}{space 2} .1554311{col 64}{space 1}    0.61{col 73}{space 3}0.540{col 81}{space 4}-.2093531{col 94}{space 3} .3999255
{txt}{space 32}russian {c |}{col 41}{res}{space 2}-.0476016{col 53}{space 2} .2113272{col 64}{space 1}   -0.23{col 73}{space 3}0.822{col 81}{space 4}-.4617953{col 94}{space 3} .3665922
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2} .0837897{col 53}{space 2} .0849537{col 64}{space 1}    0.99{col 73}{space 3}0.324{col 81}{space 4}-.0827165{col 94}{space 3} .2502959
{txt}{space 33}female {c |}{col 41}{res}{space 2}-.3008621{col 53}{space 2} .0568008{col 64}{space 1}   -5.30{col 73}{space 3}0.000{col 81}{space 4}-.4121895{col 94}{space 3}-.1895346
{txt}{space 36}age {c |}{col 41}{res}{space 2}-.0023582{col 53}{space 2} .0024319{col 64}{space 1}   -0.97{col 73}{space 3}0.332{col 81}{space 4}-.0071247{col 94}{space 3} .0024083
{txt}{space 30}education {c |}{col 41}{res}{space 2} .0639022{col 53}{space 2} .0208968{col 64}{space 1}    3.06{col 73}{space 3}0.002{col 81}{space 4} .0229452{col 94}{space 3} .1048592
{txt}{space 33}income {c |}{col 41}{res}{space 2} .0263721{col 53}{space 2} .0329973{col 64}{space 1}    0.80{col 73}{space 3}0.424{col 81}{space 4}-.0383014{col 94}{space 3} .0910455
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2}-.0432452{col 53}{space 2} .1204783{col 64}{space 1}   -0.36{col 73}{space 3}0.720{col 81}{space 4}-.2793785{col 94}{space 3}  .192888
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2} .1039912{col 53}{space 2}  .102597{col 64}{space 1}    1.01{col 73}{space 3}0.311{col 81}{space 4}-.0970952{col 94}{space 3} .3050777
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2} .2665267{col 53}{space 2} .1464366{col 64}{space 1}    1.82{col 73}{space 3}0.069{col 81}{space 4}-.0204838{col 94}{space 3} .5535372
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2} .1553546{col 53}{space 2} .1372798{col 64}{space 1}    1.13{col 73}{space 3}0.258{col 81}{space 4}-.1137089{col 94}{space 3} .4244181
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2} .1426262{col 53}{space 2} .2233249{col 64}{space 1}    0.64{col 73}{space 3}0.523{col 81}{space 4}-.2950825{col 94}{space 3} .5803349
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2} .0164804{col 53}{space 2} .1216871{col 64}{space 1}    0.14{col 73}{space 3}0.892{col 81}{space 4} -.222022{col 94}{space 3} .2549828
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2} .0409088{col 53}{space 2} .1055954{col 64}{space 1}    0.39{col 73}{space 3}0.698{col 81}{space 4}-.1660543{col 94}{space 3}  .247872
{txt}{space 31}Student  {c |}{col 41}{res}{space 2} .0345901{col 53}{space 2}  .212168{col 64}{space 1}    0.16{col 73}{space 3}0.870{col 81}{space 4}-.3812516{col 94}{space 3} .4504318
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2} .0137296{col 53}{space 2} .1785155{col 64}{space 1}    0.08{col 73}{space 3}0.939{col 81}{space 4}-.3361544{col 94}{space 3} .3636135
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2} .1177738{col 53}{space 2} .0702987{col 64}{space 1}    1.68{col 73}{space 3}0.094{col 81}{space 4}-.0200092{col 94}{space 3} .2555568
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2} .1140732{col 53}{space 2} .1015175{col 64}{space 1}    1.12{col 73}{space 3}0.261{col 81}{space 4}-.0848974{col 94}{space 3} .3130438
{txt}{space 31}Central  {c |}{col 41}{res}{space 2}  .023735{col 53}{space 2} .0875664{col 64}{space 1}    0.27{col 73}{space 3}0.786{col 81}{space 4} -.147892{col 94}{space 3}  .195362
{txt}{space 33}South  {c |}{col 41}{res}{space 2} .0720852{col 53}{space 2} .0896241{col 64}{space 1}    0.80{col 73}{space 3}0.421{col 81}{space 4}-.1035749{col 94}{space 3} .2477453
{txt}{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 34}/cut1 {c |}{col 41}{res}{space 2}-1.529985{col 53}{space 2} .3226156{col 81}{space 4}  -2.1623{col 94}{space 3}-.8976702
{txt}{space 34}/cut2 {c |}{col 41}{res}{space 2}-1.476803{col 53}{space 2} .3226268{col 81}{space 4} -2.10914{col 94}{space 3}-.8444665
{txt}{space 34}/cut3 {c |}{col 41}{res}{space 2}-1.301646{col 53}{space 2} .3208384{col 81}{space 4}-1.930478{col 94}{space 3}-.6728143
{txt}{space 34}/cut4 {c |}{col 41}{res}{space 2}-1.116598{col 53}{space 2} .3202626{col 81}{space 4}-1.744301{col 94}{space 3}-.4888952
{txt}{space 34}/cut5 {c |}{col 41}{res}{space 2}-1.038686{col 53}{space 2} .3192177{col 81}{space 4}-1.664341{col 94}{space 3}-.4130303
{txt}{space 34}/cut6 {c |}{col 41}{res}{space 2} .8631289{col 53}{space 2} .3170174{col 81}{space 4} .2417863{col 94}{space 3} 1.484471
{txt}{space 34}/cut7 {c |}{col 41}{res}{space 2} 1.101383{col 53}{space 2} .3171647{col 81}{space 4} .4797515{col 94}{space 3} 1.723014
{txt}{space 34}/cut8 {c |}{col 41}{res}{space 2} 1.349579{col 53}{space 2} .3178922{col 81}{space 4} .7265217{col 94}{space 3} 1.972636
{txt}{space 34}/cut9 {c |}{col 41}{res}{space 2} 1.522067{col 53}{space 2} .3184497{col 81}{space 4} .8979167{col 94}{space 3} 2.146217
{txt}{space 33}/cut10 {c |}{col 41}{res}{space 2} 1.623236{col 53}{space 2}  .319026{col 81}{space 4} .9979569{col 94}{space 3} 2.248516
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. oprobit providemoral ib3.humanimalaidtxt i.ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4 , robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-3048.7966}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-2995.0034}  
Iteration 2:{space 2}Log pseudolikelihood = {res: -2994.983}  
Iteration 3:{space 2}Log pseudolikelihood = {res: -2994.983}  
{res}
{txt}{col 1}Ordered probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,875}
{txt}{col 57}{lalign 13:Wald chi2({res:30})}{col 70} = {res}{ralign 6:105.54}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 9:-2994.983}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0177}

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                           providemoral{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      z{col 73}   P>|z|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}humanimalaidtxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2}-.0254436{col 53}{space 2} .0613795{col 64}{space 1}   -0.41{col 73}{space 3}0.678{col 81}{space 4}-.1457453{col 94}{space 3} .0948581
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2}-.0545964{col 53}{space 2} .0614822{col 64}{space 1}   -0.89{col 73}{space 3}0.375{col 81}{space 4}-.1750993{col 94}{space 3} .0659065
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}-.2908794{col 53}{space 2} .0582323{col 64}{space 1}   -5.00{col 73}{space 3}0.000{col 81}{space 4}-.4050126{col 94}{space 3}-.1767462
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2} .2154762{col 53}{space 2} .2108669{col 64}{space 1}    1.02{col 73}{space 3}0.307{col 81}{space 4}-.1978154{col 94}{space 3} .6287677
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2}-.1819832{col 53}{space 2} .0820755{col 64}{space 1}   -2.22{col 73}{space 3}0.027{col 81}{space 4}-.3428483{col 94}{space 3}-.0211182
{txt}{space 39} {c |}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2} .0617187{col 53}{space 2} .0655251{col 64}{space 1}    0.94{col 73}{space 3}0.346{col 81}{space 4}-.0667081{col 94}{space 3} .1901455
{txt}{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2} .0536464{col 53}{space 2} .0898618{col 64}{space 1}    0.60{col 73}{space 3}0.551{col 81}{space 4}-.1224795{col 94}{space 3} .2297724
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2}-.0278223{col 53}{space 2} .0514239{col 64}{space 1}   -0.54{col 73}{space 3}0.588{col 81}{space 4}-.1286113{col 94}{space 3} .0729666
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2} .0812607{col 53}{space 2} .0705854{col 64}{space 1}    1.15{col 73}{space 3}0.250{col 81}{space 4}-.0570842{col 94}{space 3} .2196055
{txt}{space 30}displaced {c |}{col 41}{res}{space 2}-.0100174{col 53}{space 2} .0802103{col 64}{space 1}   -0.12{col 73}{space 3}0.901{col 81}{space 4}-.1672267{col 94}{space 3} .1471918
{txt}{space 30}ukrainian {c |}{col 41}{res}{space 2}-.0628741{col 53}{space 2} .1381955{col 64}{space 1}   -0.45{col 73}{space 3}0.649{col 81}{space 4}-.3337323{col 94}{space 3} .2079841
{txt}{space 32}russian {c |}{col 41}{res}{space 2} .0998399{col 53}{space 2}  .215415{col 64}{space 1}    0.46{col 73}{space 3}0.643{col 81}{space 4}-.3223657{col 94}{space 3} .5220456
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2}-.0063892{col 53}{space 2} .0858174{col 64}{space 1}   -0.07{col 73}{space 3}0.941{col 81}{space 4}-.1745881{col 94}{space 3} .1618098
{txt}{space 33}female {c |}{col 41}{res}{space 2}-.3023351{col 53}{space 2} .0550142{col 64}{space 1}   -5.50{col 73}{space 3}0.000{col 81}{space 4} -.410161{col 94}{space 3}-.1945091
{txt}{space 36}age {c |}{col 41}{res}{space 2} .0063305{col 53}{space 2} .0023076{col 64}{space 1}    2.74{col 73}{space 3}0.006{col 81}{space 4} .0018077{col 94}{space 3} .0108534
{txt}{space 30}education {c |}{col 41}{res}{space 2} .0161028{col 53}{space 2}  .020567{col 64}{space 1}    0.78{col 73}{space 3}0.434{col 81}{space 4}-.0242078{col 94}{space 3} .0564134
{txt}{space 33}income {c |}{col 41}{res}{space 2}-.0216165{col 53}{space 2} .0329517{col 64}{space 1}   -0.66{col 73}{space 3}0.512{col 81}{space 4}-.0862006{col 94}{space 3} .0429676
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2}-.0665067{col 53}{space 2} .1249324{col 64}{space 1}   -0.53{col 73}{space 3}0.594{col 81}{space 4}-.3113698{col 94}{space 3} .1783564
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2}-.0718225{col 53}{space 2} .0962078{col 64}{space 1}   -0.75{col 73}{space 3}0.455{col 81}{space 4}-.2603863{col 94}{space 3} .1167413
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2} .2820417{col 53}{space 2} .1378079{col 64}{space 1}    2.05{col 73}{space 3}0.041{col 81}{space 4} .0119432{col 94}{space 3} .5521403
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2} .0013212{col 53}{space 2} .1269152{col 64}{space 1}    0.01{col 73}{space 3}0.992{col 81}{space 4}-.2474281{col 94}{space 3} .2500705
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2} .1316412{col 53}{space 2} .2054652{col 64}{space 1}    0.64{col 73}{space 3}0.522{col 81}{space 4}-.2710632{col 94}{space 3} .5343455
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2}-.0916181{col 53}{space 2} .1222455{col 64}{space 1}   -0.75{col 73}{space 3}0.454{col 81}{space 4}-.3312148{col 94}{space 3} .1479786
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2}-.0038948{col 53}{space 2} .0991192{col 64}{space 1}   -0.04{col 73}{space 3}0.969{col 81}{space 4}-.1981649{col 94}{space 3} .1903754
{txt}{space 31}Student  {c |}{col 41}{res}{space 2} .2261987{col 53}{space 2} .2252895{col 64}{space 1}    1.00{col 73}{space 3}0.315{col 81}{space 4}-.2153607{col 94}{space 3} .6677581
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2}  .187445{col 53}{space 2} .1484256{col 64}{space 1}    1.26{col 73}{space 3}0.207{col 81}{space 4}-.1034639{col 94}{space 3} .4783539
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2} .1221299{col 53}{space 2} .0731377{col 64}{space 1}    1.67{col 73}{space 3}0.095{col 81}{space 4}-.0212175{col 94}{space 3} .2654772
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2} -.019115{col 53}{space 2} .0995932{col 64}{space 1}   -0.19{col 73}{space 3}0.848{col 81}{space 4}-.2143142{col 94}{space 3} .1760841
{txt}{space 31}Central  {c |}{col 41}{res}{space 2}-.0500068{col 53}{space 2} .0864265{col 64}{space 1}   -0.58{col 73}{space 3}0.563{col 81}{space 4}-.2193996{col 94}{space 3}  .119386
{txt}{space 33}South  {c |}{col 41}{res}{space 2}-.0641488{col 53}{space 2} .0893512{col 64}{space 1}   -0.72{col 73}{space 3}0.473{col 81}{space 4}-.2392739{col 94}{space 3} .1109764
{txt}{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 34}/cut1 {c |}{col 41}{res}{space 2} -1.07612{col 53}{space 2} .3102255{col 81}{space 4}-1.684151{col 94}{space 3}-.4680892
{txt}{space 34}/cut2 {c |}{col 41}{res}{space 2}-1.044025{col 53}{space 2} .3101332{col 81}{space 4}-1.651875{col 94}{space 3}-.4361749
{txt}{space 34}/cut3 {c |}{col 41}{res}{space 2}-.9578692{col 53}{space 2} .3100353{col 81}{space 4}-1.565527{col 94}{space 3}-.3502113
{txt}{space 34}/cut4 {c |}{col 41}{res}{space 2} -.892796{col 53}{space 2} .3097984{col 81}{space 4} -1.49999{col 94}{space 3}-.2856023
{txt}{space 34}/cut5 {c |}{col 41}{res}{space 2}-.8151899{col 53}{space 2} .3095328{col 81}{space 4}-1.421863{col 94}{space 3}-.2085168
{txt}{space 34}/cut6 {c |}{col 41}{res}{space 2} .4942225{col 53}{space 2} .3095822{col 81}{space 4}-.1125474{col 94}{space 3} 1.100992
{txt}{space 34}/cut7 {c |}{col 41}{res}{space 2} .5648981{col 53}{space 2} .3097286{col 81}{space 4}-.0421588{col 94}{space 3} 1.171955
{txt}{space 34}/cut8 {c |}{col 41}{res}{space 2} .6740541{col 53}{space 2} .3098069{col 81}{space 4} .0668437{col 94}{space 3} 1.281264
{txt}{space 34}/cut9 {c |}{col 41}{res}{space 2} .8409364{col 53}{space 2} .3100462{col 81}{space 4} .2332571{col 94}{space 3} 1.448616
{txt}{space 33}/cut10 {c |}{col 41}{res}{space 2} .8762154{col 53}{space 2} .3100858{col 81}{space 4} .2684584{col 94}{space 3} 1.483973
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. oprobit selfhelp ib3.humanimalaidtxt i.ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4 , robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-2543.0389}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-2526.0315}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-2526.0251}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-2526.0251}  
{res}
{txt}{col 1}Ordered probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,892}
{txt}{col 57}{lalign 13:Wald chi2({res:30})}{col 70} = {res}{ralign 6:35.20}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.2354}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-2526.0251}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0067}

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                               selfhelp{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      z{col 73}   P>|z|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}humanimalaidtxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2} .0021696{col 53}{space 2} .0662077{col 64}{space 1}    0.03{col 73}{space 3}0.974{col 81}{space 4} -.127595{col 94}{space 3} .1319343
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2} .0251521{col 53}{space 2} .0649361{col 64}{space 1}    0.39{col 73}{space 3}0.699{col 81}{space 4}-.1021204{col 94}{space 3} .1524245
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2} .1587038{col 53}{space 2} .0617974{col 64}{space 1}    2.57{col 73}{space 3}0.010{col 81}{space 4} .0375832{col 94}{space 3} .2798245
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2} .2744632{col 53}{space 2} .2206432{col 64}{space 1}    1.24{col 73}{space 3}0.214{col 81}{space 4}-.1579896{col 94}{space 3}  .706916
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2} .1008983{col 53}{space 2} .0862882{col 64}{space 1}    1.17{col 73}{space 3}0.242{col 81}{space 4}-.0682235{col 94}{space 3}   .27002
{txt}{space 39} {c |}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2}-.1018354{col 53}{space 2} .0688712{col 64}{space 1}   -1.48{col 73}{space 3}0.139{col 81}{space 4}-.2368204{col 94}{space 3} .0331497
{txt}{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2}-.0403657{col 53}{space 2} .0986047{col 64}{space 1}   -0.41{col 73}{space 3}0.682{col 81}{space 4}-.2336274{col 94}{space 3}  .152896
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2}  -.00843{col 53}{space 2} .0552671{col 64}{space 1}   -0.15{col 73}{space 3}0.879{col 81}{space 4}-.1167515{col 94}{space 3} .0998915
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2} .0094741{col 53}{space 2} .0790925{col 64}{space 1}    0.12{col 73}{space 3}0.905{col 81}{space 4}-.1455443{col 94}{space 3} .1644925
{txt}{space 30}displaced {c |}{col 41}{res}{space 2} .0660374{col 53}{space 2} .0841932{col 64}{space 1}    0.78{col 73}{space 3}0.433{col 81}{space 4}-.0989783{col 94}{space 3} .2310531
{txt}{space 30}ukrainian {c |}{col 41}{res}{space 2} .3371775{col 53}{space 2} .1574534{col 64}{space 1}    2.14{col 73}{space 3}0.032{col 81}{space 4} .0285744{col 94}{space 3} .6457805
{txt}{space 32}russian {c |}{col 41}{res}{space 2}   .10204{col 53}{space 2} .2226013{col 64}{space 1}    0.46{col 73}{space 3}0.647{col 81}{space 4}-.3342506{col 94}{space 3} .5383306
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2} .0532372{col 53}{space 2} .0947349{col 64}{space 1}    0.56{col 73}{space 3}0.574{col 81}{space 4}-.1324399{col 94}{space 3} .2389143
{txt}{space 33}female {c |}{col 41}{res}{space 2} .0756014{col 53}{space 2} .0578746{col 64}{space 1}    1.31{col 73}{space 3}0.191{col 81}{space 4}-.0378307{col 94}{space 3} .1890335
{txt}{space 36}age {c |}{col 41}{res}{space 2} .0032601{col 53}{space 2} .0024765{col 64}{space 1}    1.32{col 73}{space 3}0.188{col 81}{space 4}-.0015937{col 94}{space 3} .0081138
{txt}{space 30}education {c |}{col 41}{res}{space 2}-.0126208{col 53}{space 2} .0209082{col 64}{space 1}   -0.60{col 73}{space 3}0.546{col 81}{space 4}   -.0536{col 94}{space 3} .0283584
{txt}{space 33}income {c |}{col 41}{res}{space 2} .0377614{col 53}{space 2} .0352066{col 64}{space 1}    1.07{col 73}{space 3}0.283{col 81}{space 4}-.0312424{col 94}{space 3} .1067652
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2} .0843052{col 53}{space 2} .1261306{col 64}{space 1}    0.67{col 73}{space 3}0.504{col 81}{space 4}-.1629062{col 94}{space 3} .3315166
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2} .0552685{col 53}{space 2}  .103576{col 64}{space 1}    0.53{col 73}{space 3}0.594{col 81}{space 4}-.1477368{col 94}{space 3} .2582738
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2} .0229939{col 53}{space 2} .1452058{col 64}{space 1}    0.16{col 73}{space 3}0.874{col 81}{space 4}-.2616042{col 94}{space 3}  .307592
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2}-.0714736{col 53}{space 2}  .134956{col 64}{space 1}   -0.53{col 73}{space 3}0.596{col 81}{space 4}-.3359825{col 94}{space 3} .1930354
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2} .1898996{col 53}{space 2} .2115709{col 64}{space 1}    0.90{col 73}{space 3}0.369{col 81}{space 4}-.2247718{col 94}{space 3}  .604571
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2}-.0230182{col 53}{space 2} .1355034{col 64}{space 1}   -0.17{col 73}{space 3}0.865{col 81}{space 4}   -.2886{col 94}{space 3} .2425636
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2} .0463776{col 53}{space 2} .1099372{col 64}{space 1}    0.42{col 73}{space 3}0.673{col 81}{space 4}-.1690953{col 94}{space 3} .2618506
{txt}{space 31}Student  {c |}{col 41}{res}{space 2}-.1862878{col 53}{space 2} .2119223{col 64}{space 1}   -0.88{col 73}{space 3}0.379{col 81}{space 4} -.601648{col 94}{space 3} .2290723
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2}-.1349043{col 53}{space 2}  .169438{col 64}{space 1}   -0.80{col 73}{space 3}0.426{col 81}{space 4}-.4669967{col 94}{space 3} .1971881
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2}-.0920198{col 53}{space 2} .0785124{col 64}{space 1}   -1.17{col 73}{space 3}0.241{col 81}{space 4}-.2459012{col 94}{space 3} .0618617
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2} .0513986{col 53}{space 2} .1052773{col 64}{space 1}    0.49{col 73}{space 3}0.625{col 81}{space 4} -.154941{col 94}{space 3} .2577383
{txt}{space 31}Central  {c |}{col 41}{res}{space 2} .0983996{col 53}{space 2} .0930504{col 64}{space 1}    1.06{col 73}{space 3}0.290{col 81}{space 4}-.0839758{col 94}{space 3} .2807751
{txt}{space 33}South  {c |}{col 41}{res}{space 2} .1432822{col 53}{space 2} .0958273{col 64}{space 1}    1.50{col 73}{space 3}0.135{col 81}{space 4}-.0445359{col 94}{space 3} .3311002
{txt}{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 34}/cut1 {c |}{col 41}{res}{space 2}-1.136946{col 53}{space 2} .3253555{col 81}{space 4}-1.774631{col 94}{space 3}-.4992607
{txt}{space 34}/cut2 {c |}{col 41}{res}{space 2}-1.088311{col 53}{space 2} .3242897{col 81}{space 4}-1.723907{col 94}{space 3}-.4527147
{txt}{space 34}/cut3 {c |}{col 41}{res}{space 2}-1.013842{col 53}{space 2} .3229391{col 81}{space 4}-1.646792{col 94}{space 3}-.3808934
{txt}{space 34}/cut4 {c |}{col 41}{res}{space 2}-.9477021{col 53}{space 2} .3237456{col 81}{space 4}-1.582232{col 94}{space 3}-.3131724
{txt}{space 34}/cut5 {c |}{col 41}{res}{space 2}-.8926442{col 53}{space 2} .3226091{col 81}{space 4}-1.524946{col 94}{space 3} -.260342
{txt}{space 34}/cut6 {c |}{col 41}{res}{space 2} .0850581{col 53}{space 2} .3195438{col 81}{space 4}-.5412362{col 94}{space 3} .7113525
{txt}{space 34}/cut7 {c |}{col 41}{res}{space 2} .1093114{col 53}{space 2} .3194808{col 81}{space 4}-.5168593{col 94}{space 3} .7354822
{txt}{space 34}/cut8 {c |}{col 41}{res}{space 2} .2229789{col 53}{space 2} .3196715{col 81}{space 4}-.4035657{col 94}{space 3} .8495236
{txt}{space 34}/cut9 {c |}{col 41}{res}{space 2} .4348339{col 53}{space 2} .3198021{col 81}{space 4}-.1919667{col 94}{space 3} 1.061635
{txt}{space 33}/cut10 {c |}{col 41}{res}{space 2} .5207192{col 53}{space 2} .3198567{col 81}{space 4}-.1061884{col 94}{space 3} 1.147627
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. oprobit giveidp100rnd ib3.humanimalaidtxt i.ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4 , robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-3393.7503}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-3267.3209}  
Iteration 2:{space 2}Log pseudolikelihood = {res:  -3267.22}  
Iteration 3:{space 2}Log pseudolikelihood = {res:  -3267.22}  
{res}
{txt}{col 1}Ordered probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,906}
{txt}{col 57}{lalign 13:Wald chi2({res:30})}{col 70} = {res}{ralign 6:233.61}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 8:-3267.22}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0373}

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                          giveidp100rnd{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      z{col 73}   P>|z|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}humanimalaidtxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2}-.0198971{col 53}{space 2} .0589153{col 64}{space 1}   -0.34{col 73}{space 3}0.736{col 81}{space 4}-.1353689{col 94}{space 3} .0955746
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2}-.1372651{col 53}{space 2} .0597799{col 64}{space 1}   -2.30{col 73}{space 3}0.022{col 81}{space 4}-.2544315{col 94}{space 3}-.0200986
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}-.3761614{col 53}{space 2} .0572931{col 64}{space 1}   -6.57{col 73}{space 3}0.000{col 81}{space 4}-.4884538{col 94}{space 3} -.263869
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2}  .017881{col 53}{space 2} .1778919{col 64}{space 1}    0.10{col 73}{space 3}0.920{col 81}{space 4}-.3307808{col 94}{space 3} .3665428
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2}-.3691051{col 53}{space 2} .0811677{col 64}{space 1}   -4.55{col 73}{space 3}0.000{col 81}{space 4} -.528191{col 94}{space 3}-.2100193
{txt}{space 39} {c |}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2}   .04577{col 53}{space 2} .0666979{col 64}{space 1}    0.69{col 73}{space 3}0.493{col 81}{space 4}-.0849555{col 94}{space 3} .1764954
{txt}{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2}-.2068343{col 53}{space 2} .0944805{col 64}{space 1}   -2.19{col 73}{space 3}0.029{col 81}{space 4}-.3920127{col 94}{space 3} -.021656
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2}-.0290419{col 53}{space 2} .0489576{col 64}{space 1}   -0.59{col 73}{space 3}0.553{col 81}{space 4} -.124997{col 94}{space 3} .0669132
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2} .0732333{col 53}{space 2} .0714116{col 64}{space 1}    1.03{col 73}{space 3}0.305{col 81}{space 4} -.066731{col 94}{space 3} .2131975
{txt}{space 30}displaced {c |}{col 41}{res}{space 2} .0397866{col 53}{space 2} .0759793{col 64}{space 1}    0.52{col 73}{space 3}0.601{col 81}{space 4}  -.10913{col 94}{space 3} .1887032
{txt}{space 30}ukrainian {c |}{col 41}{res}{space 2} .0412919{col 53}{space 2} .1299629{col 64}{space 1}    0.32{col 73}{space 3}0.751{col 81}{space 4}-.2134307{col 94}{space 3} .2960145
{txt}{space 32}russian {c |}{col 41}{res}{space 2}-.0598735{col 53}{space 2} .1985255{col 64}{space 1}   -0.30{col 73}{space 3}0.763{col 81}{space 4}-.4489763{col 94}{space 3} .3292294
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2} .1033535{col 53}{space 2} .0807414{col 64}{space 1}    1.28{col 73}{space 3}0.201{col 81}{space 4}-.0548968{col 94}{space 3} .2616038
{txt}{space 33}female {c |}{col 41}{res}{space 2}-.5993904{col 53}{space 2} .0541452{col 64}{space 1}  -11.07{col 73}{space 3}0.000{col 81}{space 4} -.705513{col 94}{space 3}-.4932677
{txt}{space 36}age {c |}{col 41}{res}{space 2} .0042742{col 53}{space 2} .0023211{col 64}{space 1}    1.84{col 73}{space 3}0.066{col 81}{space 4}-.0002751{col 94}{space 3} .0088236
{txt}{space 30}education {c |}{col 41}{res}{space 2} .0461576{col 53}{space 2} .0193101{col 64}{space 1}    2.39{col 73}{space 3}0.017{col 81}{space 4} .0083105{col 94}{space 3} .0840048
{txt}{space 33}income {c |}{col 41}{res}{space 2} .0173858{col 53}{space 2} .0302322{col 64}{space 1}    0.58{col 73}{space 3}0.565{col 81}{space 4}-.0418682{col 94}{space 3} .0766398
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2} .2364882{col 53}{space 2} .1213377{col 64}{space 1}    1.95{col 73}{space 3}0.051{col 81}{space 4}-.0013292{col 94}{space 3} .4743057
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2} .1998724{col 53}{space 2} .0965799{col 64}{space 1}    2.07{col 73}{space 3}0.038{col 81}{space 4} .0105794{col 94}{space 3} .3891655
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2} .4542706{col 53}{space 2} .1302659{col 64}{space 1}    3.49{col 73}{space 3}0.000{col 81}{space 4} .1989542{col 94}{space 3}  .709587
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2} .2710181{col 53}{space 2} .1190413{col 64}{space 1}    2.28{col 73}{space 3}0.023{col 81}{space 4} .0377014{col 94}{space 3} .5043348
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2} .0983085{col 53}{space 2} .1895051{col 64}{space 1}    0.52{col 73}{space 3}0.604{col 81}{space 4}-.2731147{col 94}{space 3} .4697316
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2} .1549651{col 53}{space 2} .1199552{col 64}{space 1}    1.29{col 73}{space 3}0.196{col 81}{space 4}-.0801429{col 94}{space 3}  .390073
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2} .0730115{col 53}{space 2} .0983254{col 64}{space 1}    0.74{col 73}{space 3}0.458{col 81}{space 4}-.1197028{col 94}{space 3} .2657258
{txt}{space 31}Student  {c |}{col 41}{res}{space 2} .1808173{col 53}{space 2} .1756188{col 64}{space 1}    1.03{col 73}{space 3}0.303{col 81}{space 4}-.1633892{col 94}{space 3} .5250239
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2} .2501818{col 53}{space 2}  .158901{col 64}{space 1}    1.57{col 73}{space 3}0.115{col 81}{space 4}-.0612585{col 94}{space 3} .5616222
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2} .0828978{col 53}{space 2} .0690207{col 64}{space 1}    1.20{col 73}{space 3}0.230{col 81}{space 4}-.0523802{col 94}{space 3} .2181758
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2}  .189741{col 53}{space 2} .0934166{col 64}{space 1}    2.03{col 73}{space 3}0.042{col 81}{space 4} .0066479{col 94}{space 3} .3728342
{txt}{space 31}Central  {c |}{col 41}{res}{space 2} .0602419{col 53}{space 2} .0812368{col 64}{space 1}    0.74{col 73}{space 3}0.458{col 81}{space 4}-.0989792{col 94}{space 3}  .219463
{txt}{space 33}South  {c |}{col 41}{res}{space 2} .1235029{col 53}{space 2} .0851193{col 64}{space 1}    1.45{col 73}{space 3}0.147{col 81}{space 4}-.0433278{col 94}{space 3} .2903337
{txt}{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 34}/cut1 {c |}{col 41}{res}{space 2}-1.356478{col 53}{space 2} .2948422{col 81}{space 4}-1.934358{col 94}{space 3}-.7785983
{txt}{space 34}/cut2 {c |}{col 41}{res}{space 2}-1.346394{col 53}{space 2} .2949924{col 81}{space 4}-1.924569{col 94}{space 3}-.7682199
{txt}{space 34}/cut3 {c |}{col 41}{res}{space 2}-1.293447{col 53}{space 2} .2955629{col 81}{space 4} -1.87274{col 94}{space 3}-.7141546
{txt}{space 34}/cut4 {c |}{col 41}{res}{space 2}-1.194926{col 53}{space 2} .2947941{col 81}{space 4}-1.772712{col 94}{space 3}-.6171403
{txt}{space 34}/cut5 {c |}{col 41}{res}{space 2}-1.116311{col 53}{space 2} .2953145{col 81}{space 4}-1.695117{col 94}{space 3}-.5375048
{txt}{space 34}/cut6 {c |}{col 41}{res}{space 2} .2968826{col 53}{space 2} .2955081{col 81}{space 4}-.2823025{col 94}{space 3} .8760678
{txt}{space 34}/cut7 {c |}{col 41}{res}{space 2} .5019345{col 53}{space 2} .2958133{col 81}{space 4}-.0778488{col 94}{space 3} 1.081718
{txt}{space 34}/cut8 {c |}{col 41}{res}{space 2} 1.069999{col 53}{space 2} .2969742{col 81}{space 4} .4879403{col 94}{space 3} 1.652058
{txt}{space 34}/cut9 {c |}{col 41}{res}{space 2} 1.601823{col 53}{space 2} .2993254{col 81}{space 4} 1.015156{col 94}{space 3}  2.18849
{txt}{space 33}/cut10 {c |}{col 41}{res}{space 2} 1.845672{col 53}{space 2} .3019333{col 81}{space 4} 1.253894{col 94}{space 3}  2.43745
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Experiment 2 – Correlates of Resource Bias (OLS Regression)
. 
. reg giveidp100rnd ib3.humanimalaidtxt revaidmediatorcheck, robust

{txt}Linear regression                               Number of obs     = {res}     1,951
                                                {txt}F(3, 1947)        =  {res}   148.49
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2242
                                                {txt}Root MSE          =    {res} 1.9907

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}      giveidp100rnd{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}humanimalaidtxt {c |}
{space 7}Treatment 1  {c |}{col 21}{res}{space 2}-.0505272{col 33}{space 2} .1063583{col 44}{space 1}   -0.48{col 53}{space 3}0.635{col 61}{space 4}-.2591152{col 74}{space 3} .1580608
{txt}{space 7}Treatment 2  {c |}{col 21}{res}{space 2}-.2176103{col 33}{space 2} .1125096{col 44}{space 1}   -1.93{col 53}{space 3}0.053{col 61}{space 4}-.4382622{col 74}{space 3} .0030416
{txt}{space 19} {c |}
revaidmediatorcheck {c |}{col 21}{res}{space 2} .4996169{col 33}{space 2} .0239357{col 44}{space 1}   20.87{col 53}{space 3}0.000{col 61}{space 4} .4526746{col 74}{space 3} .5465591
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}   3.3797{col 33}{space 2} .1632844{col 44}{space 1}   20.70{col 53}{space 3}0.000{col 61}{space 4} 3.059469{col 74}{space 3} 3.699931
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg giveidp100rnd ib3.humanimalaidtxt providemoral, robust

{txt}Linear regression                               Number of obs     = {res}     1,927
                                                {txt}F(3, 1923)        =  {res}    44.68
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0685
                                                {txt}Root MSE          =    {res}  2.152

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}  giveidp100rnd{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimalaidtxt {c |}
{space 3}Treatment 1  {c |}{col 17}{res}{space 2} .0044081{col 29}{space 2} .1177464{col 40}{space 1}    0.04{col 49}{space 3}0.970{col 57}{space 4}-.2265159{col 70}{space 3} .2353321
{txt}{space 3}Treatment 2  {c |}{col 17}{res}{space 2}-.2508752{col 29}{space 2} .1208435{col 40}{space 1}   -2.08{col 49}{space 3}0.038{col 57}{space 4}-.4878733{col 70}{space 3}-.0138771
{txt}{space 15} {c |}
{space 3}providemoral {c |}{col 17}{res}{space 2} .1827337{col 29}{space 2} .0160626{col 40}{space 1}   11.38{col 49}{space 3}0.000{col 57}{space 4} .1512318{col 70}{space 3} .2142356
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 5.179239{col 29}{space 2} .1210478{col 40}{space 1}   42.79{col 49}{space 3}0.000{col 57}{space 4}  4.94184{col 70}{space 3} 5.416638
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg giveidp100rnd ib3.humanimalaidtxt selfhelp, robust

{txt}Linear regression                               Number of obs     = {res}     1,943
                                                {txt}F(3, 1939)        =  {res}     7.16
                                                {txt}Prob > F          = {res}    0.0001
                                                {txt}R-squared         = {res}    0.0131
                                                {txt}Root MSE          =    {res} 2.2355

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}  giveidp100rnd{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimalaidtxt {c |}
{space 3}Treatment 1  {c |}{col 17}{res}{space 2} -.033822{col 29}{space 2} .1222955{col 40}{space 1}   -0.28{col 49}{space 3}0.782{col 57}{space 4}-.2736666{col 70}{space 3} .2060225
{txt}{space 3}Treatment 2  {c |}{col 17}{res}{space 2}-.2626424{col 29}{space 2} .1249185{col 40}{space 1}   -2.10{col 49}{space 3}0.036{col 57}{space 4}-.5076312{col 70}{space 3}-.0176536
{txt}{space 15} {c |}
{space 7}selfhelp {c |}{col 17}{res}{space 2}-.0856182{col 29}{space 2} .0208202{col 40}{space 1}   -4.11{col 49}{space 3}0.000{col 57}{space 4}-.1264504{col 70}{space 3}-.0447859
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 6.880188{col 29}{space 2} .1919556{col 40}{space 1}   35.84{col 49}{space 3}0.000{col 57}{space 4} 6.503727{col 70}{space 3} 7.256649
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg giveidp100rnd ib3.humanimalaidtxt revaidmediatorcheck providemoral selfhelp, robust

{txt}Linear regression                               Number of obs     = {res}     1,880
                                                {txt}F(5, 1874)        =  {res}   119.30
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2550
                                                {txt}Root MSE          =    {res} 1.9194

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}      giveidp100rnd{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}humanimalaidtxt {c |}
{space 7}Treatment 1  {c |}{col 21}{res}{space 2} -.003652{col 33}{space 2} .1043682{col 44}{space 1}   -0.03{col 53}{space 3}0.972{col 61}{space 4}-.2083421{col 74}{space 3} .2010381
{txt}{space 7}Treatment 2  {c |}{col 21}{res}{space 2}-.1812738{col 33}{space 2} .1098017{col 44}{space 1}   -1.65{col 53}{space 3}0.099{col 61}{space 4}-.3966203{col 74}{space 3} .0340728
{txt}{space 19} {c |}
revaidmediatorcheck {c |}{col 21}{res}{space 2} .4473731{col 33}{space 2} .0253183{col 44}{space 1}   17.67{col 53}{space 3}0.000{col 61}{space 4} .3977181{col 74}{space 3}  .497028
{txt}{space 7}providemoral {c |}{col 21}{res}{space 2} .1260558{col 33}{space 2} .0149808{col 44}{space 1}    8.41{col 53}{space 3}0.000{col 61}{space 4} .0966749{col 74}{space 3} .1554367
{txt}{space 11}selfhelp {c |}{col 21}{res}{space 2}-.0597224{col 33}{space 2} .0176359{col 44}{space 1}   -3.39{col 53}{space 3}0.001{col 61}{space 4}-.0943105{col 74}{space 3}-.0251344
{txt}{space 14}_cons {c |}{col 21}{res}{space 2} 3.419502{col 33}{space 2} .2301102{col 44}{space 1}   14.86{col 53}{space 3}0.000{col 61}{space 4} 2.968202{col 74}{space 3} 3.870801
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Experiment 2 Power Calculations
. 
. power oneway, n1(681) n2(661) n3(666) power(0.80 0.90 0.95 0.99)
{res}
{txt}Performing iteration ...
{res}
{p 0 2 2}{txt}Estimated{txt} between-group variance{txt} for one-way ANOVA{p_end}{txt}F test for group effect
{txt}{txt}{bind:H0: delta = 0}  {txt}versus  {bind:Ha: delta != 0}

  {txt}{c TLC}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c TRC}
  {txt}{c |}{txt}{txt}{ralign 8:alpha}{txt}{txt}{ralign 8:power}{txt}{txt}{ralign 8:N}{txt}{txt}{ralign 8:N_avg}{txt}{txt}{ralign 8:N1}{txt}{txt}{ralign 8:N2}{txt}{txt}{ralign 8:N3}{txt}{txt}{ralign 8:delta}{txt}{txt}{ralign 8:N_g}{txt}{txt}{ralign 8:Var_m}{txt}{txt}{ralign 8:Var_e}{txt}{txt} {c |}
  {txt}{c LT}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c RT}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.8}{res}{ralign 8:2,008}{res}{ralign 8:669.3}{res}{ralign 8:681}{res}{ralign 8:661}{res}{ralign 8:666}{res}{ralign 8:.06932}{res}{ralign 8:3}{res}{ralign 8:.00481}{res}{ralign 8:1}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.9}{res}{ralign 8:2,008}{res}{ralign 8:669.3}{res}{ralign 8:681}{res}{ralign 8:661}{res}{ralign 8:666}{res}{ralign 8:.07944}{res}{ralign 8:3}{res}{ralign 8:.00631}{res}{ralign 8:1}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.95}{res}{ralign 8:2,008}{res}{ralign 8:669.3}{res}{ralign 8:681}{res}{ralign 8:661}{res}{ralign 8:666}{res}{ralign 8:.08776}{res}{ralign 8:3}{res}{ralign 8:.0077}{res}{ralign 8:1}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.99}{res}{ralign 8:2,008}{res}{ralign 8:669.3}{res}{ralign 8:681}{res}{ralign 8:661}{res}{ralign 8:666}{res}{ralign 8:.1033}{res}{ralign 8:3}{res}{ralign 8:.01067}{res}{ralign 8:1}{txt} {c |}
  {txt}{c BLC}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c BRC}

{com}. esize twosample givebias if humanimalaidtxt~=1, by(humanimalaidtxt)

{txt}Effect size based on mean comparison

                               Obs per group:
                                 Treatment 2 =        653
                               Control Group =        658
{res}{col 1}{text}{hline 20}{c TT}{hline 12}{hline 12}{hline 12}
{col 1}{text}        Effect size{col 21}{c |}   Estimate{col 34}    [95% conf. interval]
{res}{col 1}{text}{hline 20}{c +}{hline 12}{hline 12}{hline 12}
{col 1}{text}          Cohen's {it:d}{col 21}{c |}{result}{space 2}-.1302745{col 34}{space 3}-.2386276{col 46}{space 3}-.0218718
{col 1}{text}         Hedges's {it:g}{col 21}{c |}{result}{space 2}-.1301998{col 34}{space 3}-.2384908{col 46}{space 3}-.0218593
{col 1}{text}{hline 20}{c BT}{hline 12}{hline 12}{hline 12}
{res}{txt}
{com}. 
. *Sensitivity Analysis – Experiment 2 and Correlates of Resource Allocation
. 
. reg giveidp100rnd ib3.humanimalaidtxt, robust

{txt}Linear regression                               Number of obs     = {res}     1,981
                                                {txt}F(2, 1978)        =  {res}     3.12
                                                {txt}Prob > F          = {res}    0.0445
                                                {txt}R-squared         = {res}    0.0032
                                                {txt}Root MSE          =    {res} 2.2547

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}  giveidp100rnd{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimalaidtxt {c |}
{space 3}Treatment 1  {c |}{col 17}{res}{space 2}-.0547203{col 29}{space 2} .1222233{col 40}{space 1}   -0.45{col 49}{space 3}0.654{col 57}{space 4}-.2944203{col 70}{space 3} .1849796
{txt}{space 3}Treatment 2  {c |}{col 17}{res}{space 2}-.2953914{col 29}{space 2} .1250623{col 40}{space 1}   -2.36{col 49}{space 3}0.018{col 57}{space 4} -.540659{col 70}{space 3}-.0501238
{txt}{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2} 6.221884{col 29}{space 2} .0864327{col 40}{space 1}   71.99{col 49}{space 3}0.000{col 57}{space 4} 6.052376{col 70}{space 3} 6.391393
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg giveidp100rnd i.ownpets, robust

{txt}Linear regression                               Number of obs     = {res}     1,979
                                                {txt}F(3, 1975)        =  {res}    17.97
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0263
                                                {txt}Root MSE          =    {res} 2.2299

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}              giveidp100rnd{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}ownpets {c |}
{space 22}Pets  {c |}{col 29}{res}{space 2}-.7797052{col 41}{space 2} .1144091{col 52}{space 1}   -6.82{col 61}{space 3}0.000{col 69}{space 4} -1.00408{col 82}{space 3}  -.55533
{txt}{space 14}Farm Animals  {c |}{col 29}{res}{space 2} .0572178{col 41}{space 2} .3224042{col 52}{space 1}    0.18{col 61}{space 3}0.859{col 69}{space 4}-.5750704{col 82}{space 3} .6895061
{txt}Both pets and farm animals  {c |}{col 29}{res}{space 2}-.7287149{col 41}{space 2}  .146747{col 52}{space 1}   -4.97{col 61}{space 3}0.000{col 69}{space 4} -1.01651{col 82}{space 3}-.4409197
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2} 6.609449{col 41}{space 2} .0880689{col 52}{space 1}   75.05{col 61}{space 3}0.000{col 69}{space 4} 6.436731{col 82}{space 3} 6.782167
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg giveidp100rnd female, robust

{txt}Linear regression                               Number of obs     = {res}     1,981
                                                {txt}F(1, 1979)        =  {res}   158.81
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0727
                                                {txt}Root MSE          =    {res} 2.1741

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}giveidp100~d{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}female {c |}{col 14}{res}{space 2}-1.228627{col 26}{space 2} .0974947{col 37}{space 1}  -12.60{col 46}{space 3}0.000{col 54}{space 4} -1.41983{col 67}{space 3}-1.037424
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 6.803738{col 26}{space 2} .0707401{col 37}{space 1}   96.18{col 46}{space 3}0.000{col 54}{space 4} 6.665005{col 67}{space 3} 6.942471
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. regsensitivity bounds giveidp100rnd humanimalaidtxt ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income employment region4 if humanimalaidtxt~=1, dmp  robust
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: DMP (2022){col 48}{txt}Number of obs{col 67}{res}=       1,262
{col 48}{txt}Beta(short){col 67}{res}=       0.312
{txt}Treatment{col 18}{res}: humanimalaidtxt{col 48}{txt}Beta(medium){col 67}{res}=       0.278
{txt}Outcome{col 18}{res}: giveidp100rnd{col 48}{txt}R2(short){col 67}{res}=       0.005
{col 48}{txt}R2(medium){col 67}{res}=       0.100
{col 48}{txt}Var(Y){col 67}{res}=       5.027
{col 48}{txt}Var(X){col 67}{res}=       0.250
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.249

{txt}Hypothesis{col 18}{res}: Beta > 0         {col 48}{txt}Breakdown point{col 67}{res}=        65.2%
{txt}Other Params{col 18}{res}: cbar = 1, rybar = +inf

{txt}{hline 80}
 rxbar{col 35} Beta
{hline 80}
{res}{col 2}0.000{col 35}{txt}[{res} 0.2782{txt}, {res} 0.2782{txt} ]
{col 2}{res}0.103{col 35}{txt}[{res} 0.2449{txt}, {res} 0.3115{txt} ]
{col 2}{res}0.205{col 35}{txt}[{res} 0.2105{txt}, {res} 0.3459{txt} ]
{col 2}{res}0.308{col 35}{txt}[{res} 0.1737{txt}, {res} 0.3827{txt} ]
{col 2}{res}0.411{col 35}{txt}[{res} 0.1327{txt}, {res} 0.4236{txt} ]
{col 2}{res}0.514{col 35}{txt}[{res} 0.0849{txt}, {res} 0.4715{txt} ]
{col 2}{res}0.616{col 35}{txt}[{res} 0.0253{txt}, {res} 0.5311{txt} ]
{col 2}{res}0.719{col 35}{txt}[{res}-0.0566{txt}, {res} 0.6129{txt} ]
{col 2}{res}0.822{col 35}{txt}[{res}-0.1897{txt}, {res} 0.7461{txt} ]
{col 2}{res}0.924{col 35}{txt}[{res}-0.5171{txt}, {res} 1.0734{txt} ]
{col 2}{res}0.997{col 35}{txt}[   {res}-inf{txt},    {res}+inf{txt} ]
{hline 80}

{com}. 
. regsensitivity bounds giveidp100rnd humanimalaidtxt ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income employment region4 if humanimalaidtxt~=1, oster  robust
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: Oster (2019){col 48}{txt}Number of obs{col 67}{res}=       1,262
{col 48}{txt}Beta(short){col 67}{res}=       0.312
{txt}Treatment{col 18}{res}: humanimalaidtxt{col 48}{txt}Beta(medium){col 67}{res}=       0.278
{txt}Outcome{col 18}{res}: giveidp100rnd{col 48}{txt}R2(short){col 67}{res}=       0.005
{col 48}{txt}R2(medium){col 67}{res}=       0.100
{col 48}{txt}Var(Y){col 67}{res}=       5.027
{col 48}{txt}Var(X){col 67}{res}=       0.250
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.249

{txt}Hypothesis{col 18}{res}: Beta != 0         {col 48}{txt}Breakdown point{col 67}{res}=        82.5%
{txt}Other Params{col 18}{res}: R-squared(long) = 1

{txt}{hline 80}
 Delta{col 35} Beta
{hline 80}
{res}{col 2}-0.990{col 35}{txt}{{res}   0.59{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.800{col 35}{txt}{{res}   0.53{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.600{col 35}{txt}{{res}   0.47{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.400{col 35}{txt}{{res}   0.41{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.200{col 35}{txt}{{res}   0.34{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.000{col 35}{txt}{{res}   0.28{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.200{col 35}{txt}{{res}   0.21{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.400{col 35}{txt}{{res}   0.15{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.600{col 35}{txt}{{res}   0.08{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.800{col 35}{txt}{{res}   0.01{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.990{col 35}{txt}{{res}  -0.06{txt}, {res}  58.74{txt}, {res} 550.59{txt} }
{hline 80}

{com}. 
. regsensitivity bounds giveidp100rnd ownpets humanimalaidtxt dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income employment region4 if ownpets<2, dmp robust
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: DMP (2022){col 48}{txt}Number of obs{col 67}{res}=       1,555
{col 48}{txt}Beta(short){col 67}{res}=      -0.781
{txt}Treatment{col 18}{res}: ownpets{col 48}{txt}Beta(medium){col 67}{res}=      -0.669
{txt}Outcome{col 18}{res}: giveidp100rnd{col 48}{txt}R2(short){col 67}{res}=       0.028
{col 48}{txt}R2(medium){col 67}{res}=       0.105
{col 48}{txt}Var(Y){col 67}{res}=       5.167
{col 48}{txt}Var(X){col 67}{res}=       0.239
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.230

{txt}Hypothesis{col 18}{res}: Beta < 0         {col 48}{txt}Breakdown point{col 67}{res}=        58.7%
{txt}Other Params{col 18}{res}: cbar = 1, rybar = +inf

{txt}{hline 80}
 rxbar{col 35} Beta
{hline 80}
{res}{col 2}0.000{col 35}{txt}[{res}-0.6688{txt}, {res}-0.6688{txt} ]
{col 2}{res}0.099{col 35}{txt}[{res}-0.7592{txt}, {res}-0.5783{txt} ]
{col 2}{res}0.197{col 35}{txt}[{res}-0.8526{txt}, {res}-0.4849{txt} ]
{col 2}{res}0.296{col 35}{txt}[{res}-0.9521{txt}, {res}-0.3854{txt} ]
{col 2}{res}0.395{col 35}{txt}[{res}-1.0622{txt}, {res}-0.2753{txt} ]
{col 2}{res}0.493{col 35}{txt}[{res}-1.1897{txt}, {res}-0.1478{txt} ]
{col 2}{res}0.592{col 35}{txt}[{res}-1.3466{txt}, {res} 0.0091{txt} ]
{col 2}{res}0.691{col 35}{txt}[{res}-1.5571{txt}, {res} 0.2196{txt} ]
{col 2}{res}0.789{col 35}{txt}[{res}-1.8841{txt}, {res} 0.5466{txt} ]
{col 2}{res}0.888{col 35}{txt}[{res}-2.5848{txt}, {res} 1.2472{txt} ]
{col 2}{res}0.980{col 35}{txt}[   {res}-inf{txt},    {res}+inf{txt} ]
{hline 80}

{com}. 
. regsensitivity bounds giveidp100rnd ownpets humanimalaidtxt dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income employment region4 if ownpets<2, oster robust
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: Oster (2019){col 48}{txt}Number of obs{col 67}{res}=       1,555
{col 48}{txt}Beta(short){col 67}{res}=      -0.781
{txt}Treatment{col 18}{res}: ownpets{col 48}{txt}Beta(medium){col 67}{res}=      -0.669
{txt}Outcome{col 18}{res}: giveidp100rnd{col 48}{txt}R2(short){col 67}{res}=       0.028
{col 48}{txt}R2(medium){col 67}{res}=       0.105
{col 48}{txt}Var(Y){col 67}{res}=       5.167
{col 48}{txt}Var(X){col 67}{res}=       0.239
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.230

{txt}Hypothesis{col 18}{res}: Beta != 0         {col 48}{txt}Breakdown point{col 67}{res}=        42.8%
{txt}Other Params{col 18}{res}: R-squared(long) = 1

{txt}{hline 80}
 Delta{col 35} Beta
{hline 80}
{res}{col 2}-0.990{col 35}{txt}{{res}  -1.69{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.800{col 35}{txt}{{res}  -1.54{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.600{col 35}{txt}{{res}  -1.36{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.400{col 35}{txt}{{res}  -1.15{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.200{col 35}{txt}{{res}  -0.93{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.000{col 35}{txt}{{res}  -0.67{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.200{col 35}{txt}{{res}  -0.38{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.400{col 35}{txt}{{res}  -0.05{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.600{col 35}{txt}{{res}   0.33{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.800{col 35}{txt}{{res}   0.79{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.990{col 35}{txt}{{res}-277.86{txt}, {res} -10.66{txt}, {res}   1.36{txt} }
{hline 80}

{com}. 
. regsensitivity bounds giveidp100rnd female ownpets humanimalaidtxt dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker age education income employment region4, dmp robust
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: DMP (2022){col 48}{txt}Number of obs{col 67}{res}=       1,906
{col 48}{txt}Beta(short){col 67}{res}=      -1.206
{txt}Treatment{col 18}{res}: female{col 48}{txt}Beta(medium){col 67}{res}=      -1.206
{txt}Outcome{col 18}{res}: giveidp100rnd{col 48}{txt}R2(short){col 67}{res}=       0.071
{col 48}{txt}R2(medium){col 67}{res}=       0.092
{col 48}{txt}Var(Y){col 67}{res}=       5.023
{col 48}{txt}Var(X){col 67}{res}=       0.245
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.228

{txt}Hypothesis{col 18}{res}: Beta < 0         {col 48}{txt}Breakdown point{col 67}{res}=        69.4%
{txt}Other Params{col 18}{res}: cbar = 1, rybar = +inf

{txt}{hline 80}
 rxbar{col 35} Beta
{hline 80}
{res}{col 2}0.000{col 35}{txt}[{res}-1.2060{txt}, {res}-1.2060{txt} ]
{col 2}{res}0.097{col 35}{txt}[{res}-1.3235{txt}, {res}-1.0886{txt} ]
{col 2}{res}0.193{col 35}{txt}[{res}-1.4447{txt}, {res}-0.9674{txt} ]
{col 2}{res}0.290{col 35}{txt}[{res}-1.5737{txt}, {res}-0.8384{txt} ]
{col 2}{res}0.387{col 35}{txt}[{res}-1.7163{txt}, {res}-0.6957{txt} ]
{col 2}{res}0.483{col 35}{txt}[{res}-1.8812{txt}, {res}-0.5308{txt} ]
{col 2}{res}0.580{col 35}{txt}[{res}-2.0834{txt}, {res}-0.3286{txt} ]
{col 2}{res}0.677{col 35}{txt}[{res}-2.3534{txt}, {res}-0.0587{txt} ]
{col 2}{res}0.773{col 35}{txt}[{res}-2.7686{txt}, {res} 0.3565{txt} ]
{col 2}{res}0.870{col 35}{txt}[{res}-3.6343{txt}, {res} 1.2222{txt} ]
{col 2}{res}0.965{col 35}{txt}[   {res}-inf{txt},    {res}+inf{txt} ]
{hline 80}

{com}. 
. regsensitivity bounds giveidp100rnd female ownpets humanimalaidtxt dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker age education income employment region4, oster robust
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: Oster (2019){col 48}{txt}Number of obs{col 67}{res}=       1,906
{col 48}{txt}Beta(short){col 67}{res}=      -1.206
{txt}Treatment{col 18}{res}: female{col 48}{txt}Beta(medium){col 67}{res}=      -1.206
{txt}Outcome{col 18}{res}: giveidp100rnd{col 48}{txt}R2(short){col 67}{res}=       0.071
{col 48}{txt}R2(medium){col 67}{res}=       0.092
{col 48}{txt}Var(Y){col 67}{res}=       5.023
{col 48}{txt}Var(X){col 67}{res}=       0.245
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.228

{txt}Hypothesis{col 18}{res}: Beta != 0         {col 48}{txt}Breakdown point{col 67}{res}=        36.4%
{txt}Other Params{col 18}{res}: R-squared(long) = 1

{txt}{hline 80}
 Delta{col 35} Beta
{hline 80}
{res}{col 2}-0.990{col 35}{txt}{{res} -1.21{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}-0.800{col 35}{txt}{{res} -1.21{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}-0.600{col 35}{txt}{{res} -1.21{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}-0.400{col 35}{txt}{{res} -1.21{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}-0.200{col 35}{txt}{{res} -1.21{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}0.000{col 35}{txt}{{res} -1.21{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}0.200{col 35}{txt}{{res} -1.21{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}0.400{col 35}{txt}{{res} -2.86{txt}, {res} -1.21{txt}, {res}  0.44{txt} }
{col 2}{res}0.600{col 35}{txt}{{res} -4.96{txt}, {res} -1.21{txt}, {res}  2.54{txt} }
{col 2}{res}0.800{col 35}{txt}{{res} -8.14{txt}, {res} -1.21{txt}, {res}  5.73{txt} }
{col 2}{res}0.990{col 35}{txt}{{res}-37.84{txt}, {res} -1.21{txt}, {res} 35.40{txt} }
{hline 80}

{com}. 
. *Experiment 2 Mediation Analysis
. 
. *Generating complier variables (already coded)
. 
. *gen agreenudge2 = 1 if humaidless>5 & humaidless~=.
. *replace agreenudge2 = 1 if humaidequal>5 & humaidequal~=. & agreenudge2==.
. *replace agreenudge2 = 1 if humaidmore>5 & humaidmore~=. & agreenudge2==.
. *replace agreenudge2 = 0 if agreenudge2==.
. 
. *gen humaidlessagree = 1 if aidmediatortxt==1 & agreenudge2==1
. *replace humaidlessagree = 0 if aidmediatortxt==1 & agreenudge2==0
. 
. *gen humaidequalagree = 1 if aidmediatortxt==2 & agreenudge2==1
. *replace humaidequalagree = 0 if aidmediatortxt==2 & agreenudge2==0
. 
. *gen humaidmoreagree = 1 if aidmediatortxt==3 & agreenudge2==1
. *replace humaidmoreagree = 0 if aidmediatortxt==3 & agreenudge2==0
. 
. *In total in GROUP C there were 16% Compliers in the Zoocentric Treatment, 59% Compliers in the Biocentric Treatment, and 47% Compliers in the Anthropocentric treatment. 
. 
. tab agreenudge2 if aidmediatortxt==1 & q456order==3

{txt}agreenudge2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        214       83.59       83.59
{txt}          1 {c |}{res}         42       16.41      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        256      100.00
{txt}
{com}. tab agreenudge2 if aidmediatortxt==2 & q456order==3

{txt}agreenudge2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         82       40.80       40.80
{txt}          1 {c |}{res}        119       59.20      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        201      100.00
{txt}
{com}. tab agreenudge2 if aidmediatortxt==3 & q456order==3

{txt}agreenudge2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         99       53.23       53.23
{txt}          1 {c |}{res}         87       46.77      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        186      100.00
{txt}
{com}. 
. *Balance tests indicate successful randomization into 3 Groups A, B, and C. Group means are below and t-tests are provided on the following page. 
. 
. iebaltab ownpets dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income employment urban_rural region4 , groupvar(q456order) savexlsx(balanceg2)

{res}{phang}Balance table saved in Excel format to: {browse "balanceg2.xlsx":balanceg2.xlsx}{p_end}
{txt}
{com}. 
. *Randomized Manipulation of Mediator (Group C)
. 
. iebaltab ownpets dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income employment urban_rural region4 if q456order==3, groupvar(aidmediatortxt) savexlsx(balancem2)

{res}{phang}Balance table saved in Excel format to: {browse "balancem2.xlsx":balancem2.xlsx}{p_end}
{txt}
{com}. 
. *Experimental Treatment Effects by Group (OLS Regression)
. 
. reg giveidp100rnd ib3.humanimalaidtxt if q456order==1, robust

{txt}Linear regression                               Number of obs     = {res}       669
                                                {txt}F(2, 666)         =  {res}     4.55
                                                {txt}Prob > F          = {res}    0.0109
                                                {txt}R-squared         = {res}    0.0132
                                                {txt}Root MSE          =    {res} 2.2608

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}  giveidp100rnd{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimalaidtxt {c |}
{space 3}Treatment 1  {c |}{col 17}{res}{space 2} .2165592{col 29}{space 2} .2087339{col 40}{space 1}    1.04{col 49}{space 3}0.300{col 57}{space 4}-.1932965{col 70}{space 3} .6264148
{txt}{space 3}Treatment 2  {c |}{col 17}{res}{space 2}-.4202178{col 29}{space 2} .2206669{col 40}{space 1}   -1.90{col 49}{space 3}0.057{col 57}{space 4}-.8535044{col 70}{space 3} .0130689
{txt}{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2} 6.204255{col 29}{space 2} .1534292{col 40}{space 1}   40.44{col 49}{space 3}0.000{col 57}{space 4} 5.902992{col 70}{space 3} 6.505519
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg giveidp100rnd ib3.humanimalaidtxt if q456order==2, robust

{txt}Linear regression                               Number of obs     = {res}       673
                                                {txt}F(2, 670)         =  {res}     1.23
                                                {txt}Prob > F          = {res}    0.2921
                                                {txt}R-squared         = {res}    0.0037
                                                {txt}Root MSE          =    {res} 2.3239

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}  giveidp100rnd{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimalaidtxt {c |}
{space 3}Treatment 1  {c |}{col 17}{res}{space 2}-.3174865{col 29}{space 2} .2190771{col 40}{space 1}   -1.45{col 49}{space 3}0.148{col 57}{space 4}-.7476469{col 70}{space 3} .1126738
{txt}{space 3}Treatment 2  {c |}{col 17}{res}{space 2}-.2767861{col 29}{space 2}  .220418{col 40}{space 1}   -1.26{col 49}{space 3}0.210{col 57}{space 4}-.7095793{col 70}{space 3} .1560072
{txt}{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2} 6.220264{col 29}{space 2} .1564098{col 40}{space 1}   39.77{col 49}{space 3}0.000{col 57}{space 4} 5.913152{col 70}{space 3} 6.527377
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg giveidp100rnd ib3.humanimalaidtxt if q456order==3, robust

{txt}Linear regression                               Number of obs     = {res}       639
                                                {txt}F(2, 636)         =  {res}     0.44
                                                {txt}Prob > F          = {res}    0.6431
                                                {txt}R-squared         = {res}    0.0013
                                                {txt}Root MSE          =    {res} 2.1703

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}  giveidp100rnd{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanimalaidtxt {c |}
{space 3}Treatment 1  {c |}{col 17}{res}{space 2}-.0732241{col 29}{space 2} .2023496{col 40}{space 1}   -0.36{col 49}{space 3}0.718{col 57}{space 4}-.4705782{col 70}{space 3} .3241299
{txt}{space 3}Treatment 2  {c |}{col 17}{res}{space 2} -.192517{col 29}{space 2} .2050398{col 40}{space 1}   -0.94{col 49}{space 3}0.348{col 57}{space 4}-.5951538{col 70}{space 3} .2101198
{txt}{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2} 6.244898{col 29}{space 2} .1333824{col 40}{space 1}   46.82{col 49}{space 3}0.000{col 57}{space 4} 5.982975{col 70}{space 3} 6.506821
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Manipulation of Human Empathy in GROUP C (OLS Regression)
. 
. reg revaidmediatorcheck ib3.humanimalaidtxt ib2.aidmediatortxt##agreenudge2 if q456order==3, robust

{txt}Linear regression                               Number of obs     = {res}       626
                                                {txt}F(7, 618)         =  {res}     3.80
                                                {txt}Prob > F          = {res}    0.0005
                                                {txt}R-squared         = {res}    0.0426
                                                {txt}Root MSE          =    {res} 2.1082

{txt}{hline 63}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 64}{c |}{col 76}    Robust
{col 1}                                           revaidmediatorcheck{col 64}{c |} Coefficient{col 76}  std. err.{col 88}      t{col 96}   P>|t|{col 104}     [95% con{col 117}f. interval]
{hline 63}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 47}humanimalaidtxt {c |}
{space 50}Treatment 1  {c |}{col 64}{res}{space 2}-.1273488{col 76}{space 2} .2077737{col 87}{space 1}   -0.61{col 96}{space 3}0.540{col 104}{space 4}-.5353768{col 117}{space 3} .2806793
{txt}{space 50}Treatment 2  {c |}{col 64}{res}{space 2}-.0839498{col 76}{space 2} .2051842{col 87}{space 1}   -0.41{col 96}{space 3}0.683{col 104}{space 4}-.4868925{col 117}{space 3}  .318993
{txt}{space 62} {c |}
{space 48}aidmediatortxt {c |}
{space 2}(Variant 1) S4.1. Providing resources to care for displaced  {c |}{col 64}{res}{space 2}-.6591253{col 76}{space 2} .2974878{col 87}{space 1}   -2.22{col 96}{space 3}0.027{col 104}{space 4}-1.243335{col 117}{space 3}-.0749157
{txt}{space 2}(Variant 3) S4.3. Providing resources to care for displaced  {c |}{col 64}{res}{space 2}-1.244478{col 76}{space 2} .3158952{col 87}{space 1}   -3.94{col 96}{space 3}0.000{col 104}{space 4}-1.864836{col 117}{space 3}-.6241202
{txt}{space 62} {c |}
{space 49}1.agreenudge2 {c |}{col 64}{res}{space 2}-1.109434{col 76}{space 2} .3171565{col 87}{space 1}   -3.50{col 96}{space 3}0.001{col 104}{space 4}-1.732269{col 117}{space 3} -.486599
{txt}{space 62} {c |}
{space 36}aidmediatortxt#agreenudge2 {c |}
(Variant 1) S4.1. Providing resources to care for displaced#1  {c |}{col 64}{res}{space 2} .6513215{col 76}{space 2} .4564773{col 87}{space 1}    1.43{col 96}{space 3}0.154{col 104}{space 4}-.2451131{col 117}{space 3} 1.547756
{txt}(Variant 3) S4.3. Providing resources to care for displaced#1  {c |}{col 64}{res}{space 2} 2.208438{col 76}{space 2} .4648438{col 87}{space 1}    4.75{col 96}{space 3}0.000{col 104}{space 4} 1.295573{col 117}{space 3} 3.121303
{txt}{space 62} {c |}
{space 57}_cons {c |}{col 64}{res}{space 2} 6.486236{col 76}{space 2} .2749854{col 87}{space 1}   23.59{col 96}{space 3}0.000{col 104}{space 4} 5.946217{col 117}{space 3} 7.026255
{txt}{hline 63}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg giveidp100rnd ib3.humanimalaidtxt ib2.aidmediatortxt##agreenudge2 if q456order==3, robust

{txt}Linear regression                               Number of obs     = {res}       639
                                                {txt}F(7, 631)         =  {res}     5.35
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0532
                                                {txt}Root MSE          =    {res} 2.1216

{txt}{hline 63}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 64}{c |}{col 76}    Robust
{col 1}                                                 giveidp100rnd{col 64}{c |} Coefficient{col 76}  std. err.{col 88}      t{col 96}   P>|t|{col 104}     [95% con{col 117}f. interval]
{hline 63}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 47}humanimalaidtxt {c |}
{space 50}Treatment 1  {c |}{col 64}{res}{space 2}-.0954949{col 76}{space 2} .2051251{col 87}{space 1}   -0.47{col 96}{space 3}0.642{col 104}{space 4}-.4983054{col 117}{space 3} .3073156
{txt}{space 50}Treatment 2  {c |}{col 64}{res}{space 2}-.1450228{col 76}{space 2} .2001374{col 87}{space 1}   -0.72{col 96}{space 3}0.469{col 104}{space 4}-.5380389{col 117}{space 3} .2479932
{txt}{space 62} {c |}
{space 48}aidmediatortxt {c |}
{space 2}(Variant 1) S4.1. Providing resources to care for displaced  {c |}{col 64}{res}{space 2}-.7797768{col 76}{space 2} .2651199{col 87}{space 1}   -2.94{col 96}{space 3}0.003{col 104}{space 4}-1.300401{col 117}{space 3}-.2591529
{txt}{space 2}(Variant 3) S4.3. Providing resources to care for displaced  {c |}{col 64}{res}{space 2}-1.591592{col 76}{space 2} .3071587{col 87}{space 1}   -5.18{col 96}{space 3}0.000{col 104}{space 4}-2.194769{col 117}{space 3}-.9884153
{txt}{space 62} {c |}
{space 49}1.agreenudge2 {c |}{col 64}{res}{space 2}-1.002175{col 76}{space 2} .3124961{col 87}{space 1}   -3.21{col 96}{space 3}0.001{col 104}{space 4}-1.615833{col 117}{space 3} -.388517
{txt}{space 62} {c |}
{space 36}aidmediatortxt#agreenudge2 {c |}
(Variant 1) S4.1. Providing resources to care for displaced#1  {c |}{col 64}{res}{space 2} .6726732{col 76}{space 2} .4793231{col 87}{space 1}    1.40{col 96}{space 3}0.161{col 104}{space 4}-.2685884{col 117}{space 3} 1.613935
{txt}(Variant 3) S4.3. Providing resources to care for displaced#1  {c |}{col 64}{res}{space 2} 2.425531{col 76}{space 2} .4350637{col 87}{space 1}    5.58{col 96}{space 3}0.000{col 104}{space 4} 1.571183{col 117}{space 3} 3.279878
{txt}{space 62} {c |}
{space 57}_cons {c |}{col 64}{res}{space 2} 7.023879{col 76}{space 2} .2404897{col 87}{space 1}   29.21{col 96}{space 3}0.000{col 104}{space 4} 6.551622{col 117}{space 3} 7.496136
{txt}{hline 63}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Anthropocentric Empathy Manipulation Check (Figure)
. 
. reg revaidmediatorcheck ib3.humanimalaidtxt ib2.aidmediatortxt##agreenudge2  if q456order==3, robust

{txt}Linear regression                               Number of obs     = {res}       626
                                                {txt}F(7, 618)         =  {res}     3.80
                                                {txt}Prob > F          = {res}    0.0005
                                                {txt}R-squared         = {res}    0.0426
                                                {txt}Root MSE          =    {res} 2.1082

{txt}{hline 63}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 64}{c |}{col 76}    Robust
{col 1}                                           revaidmediatorcheck{col 64}{c |} Coefficient{col 76}  std. err.{col 88}      t{col 96}   P>|t|{col 104}     [95% con{col 117}f. interval]
{hline 63}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 47}humanimalaidtxt {c |}
{space 50}Treatment 1  {c |}{col 64}{res}{space 2}-.1273488{col 76}{space 2} .2077737{col 87}{space 1}   -0.61{col 96}{space 3}0.540{col 104}{space 4}-.5353768{col 117}{space 3} .2806793
{txt}{space 50}Treatment 2  {c |}{col 64}{res}{space 2}-.0839498{col 76}{space 2} .2051842{col 87}{space 1}   -0.41{col 96}{space 3}0.683{col 104}{space 4}-.4868925{col 117}{space 3}  .318993
{txt}{space 62} {c |}
{space 48}aidmediatortxt {c |}
{space 2}(Variant 1) S4.1. Providing resources to care for displaced  {c |}{col 64}{res}{space 2}-.6591253{col 76}{space 2} .2974878{col 87}{space 1}   -2.22{col 96}{space 3}0.027{col 104}{space 4}-1.243335{col 117}{space 3}-.0749157
{txt}{space 2}(Variant 3) S4.3. Providing resources to care for displaced  {c |}{col 64}{res}{space 2}-1.244478{col 76}{space 2} .3158952{col 87}{space 1}   -3.94{col 96}{space 3}0.000{col 104}{space 4}-1.864836{col 117}{space 3}-.6241202
{txt}{space 62} {c |}
{space 49}1.agreenudge2 {c |}{col 64}{res}{space 2}-1.109434{col 76}{space 2} .3171565{col 87}{space 1}   -3.50{col 96}{space 3}0.001{col 104}{space 4}-1.732269{col 117}{space 3} -.486599
{txt}{space 62} {c |}
{space 36}aidmediatortxt#agreenudge2 {c |}
(Variant 1) S4.1. Providing resources to care for displaced#1  {c |}{col 64}{res}{space 2} .6513215{col 76}{space 2} .4564773{col 87}{space 1}    1.43{col 96}{space 3}0.154{col 104}{space 4}-.2451131{col 117}{space 3} 1.547756
{txt}(Variant 3) S4.3. Providing resources to care for displaced#1  {c |}{col 64}{res}{space 2} 2.208438{col 76}{space 2} .4648438{col 87}{space 1}    4.75{col 96}{space 3}0.000{col 104}{space 4} 1.295573{col 117}{space 3} 3.121303
{txt}{space 62} {c |}
{space 57}_cons {c |}{col 64}{res}{space 2} 6.486236{col 76}{space 2} .2749854{col 87}{space 1}   23.59{col 96}{space 3}0.000{col 104}{space 4} 5.946217{col 117}{space 3} 7.026255
{txt}{hline 63}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins ib2.aidmediatortxt#agreenudge2
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:626}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 63}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 64}{c |}{col 76} Delta-method
{col 64}{c |}     Margin{col 76}   std. err.{col 88}      t{col 96}   P>|t|{col 104}     [95% con{col 117}f. interval]
{hline 63}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 36}aidmediatortxt#agreenudge2 {c |}
(Variant 1) S4.1. Providing resources to care for displaced#0  {c |}{col 64}{res}{space 2} 5.753107{col 76}{space 2} .1414614{col 87}{space 1}   40.67{col 96}{space 3}0.000{col 104}{space 4} 5.475304{col 117}{space 3} 6.030911
{txt}(Variant 1) S4.1. Providing resources to care for displaced#1  {c |}{col 64}{res}{space 2} 5.294995{col 76}{space 2} .2985727{col 87}{space 1}   17.73{col 96}{space 3}0.000{col 104}{space 4} 4.708654{col 117}{space 3} 5.881335
{txt}(Variant 2) S4.2. Providing resources to care for displaced#0  {c |}{col 64}{res}{space 2} 6.412232{col 76}{space 2} .2607079{col 87}{space 1}   24.60{col 96}{space 3}0.000{col 104}{space 4} 5.900252{col 117}{space 3} 6.924213
{txt}(Variant 2) S4.2. Providing resources to care for displaced#1  {c |}{col 64}{res}{space 2} 5.302798{col 76}{space 2}  .179485{col 87}{space 1}   29.54{col 96}{space 3}0.000{col 104}{space 4} 4.950324{col 117}{space 3} 5.655273
{txt}(Variant 3) S4.3. Providing resources to care for displaced#0  {c |}{col 64}{res}{space 2} 5.167754{col 76}{space 2} .1817855{col 87}{space 1}   28.43{col 96}{space 3}0.000{col 104}{space 4} 4.810762{col 117}{space 3} 5.524746
{txt}(Variant 3) S4.3. Providing resources to care for displaced#1  {c |}{col 64}{res}{space 2} 6.266758{col 76}{space 2} .2888864{col 87}{space 1}   21.69{col 96}{space 3}0.000{col 104}{space 4}  5.69944{col 117}{space 3} 6.834076
{txt}{hline 63}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot 
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:aidmediatortxt agreenudge2}{p_end}
{res}{txt}
{com}. 
. *Note additional formatting required
. 
. *Support for More Human Protects (Empathy Mediator Margins Figure)
. 
. reg giveidp100rnd  ib3.humanimalaidtxt ib2.aidmediatortxt##agreenudge2 if q456order==3, robust

{txt}Linear regression                               Number of obs     = {res}       639
                                                {txt}F(7, 631)         =  {res}     5.35
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0532
                                                {txt}Root MSE          =    {res} 2.1216

{txt}{hline 63}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 64}{c |}{col 76}    Robust
{col 1}                                                 giveidp100rnd{col 64}{c |} Coefficient{col 76}  std. err.{col 88}      t{col 96}   P>|t|{col 104}     [95% con{col 117}f. interval]
{hline 63}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 47}humanimalaidtxt {c |}
{space 50}Treatment 1  {c |}{col 64}{res}{space 2}-.0954949{col 76}{space 2} .2051251{col 87}{space 1}   -0.47{col 96}{space 3}0.642{col 104}{space 4}-.4983054{col 117}{space 3} .3073156
{txt}{space 50}Treatment 2  {c |}{col 64}{res}{space 2}-.1450228{col 76}{space 2} .2001374{col 87}{space 1}   -0.72{col 96}{space 3}0.469{col 104}{space 4}-.5380389{col 117}{space 3} .2479932
{txt}{space 62} {c |}
{space 48}aidmediatortxt {c |}
{space 2}(Variant 1) S4.1. Providing resources to care for displaced  {c |}{col 64}{res}{space 2}-.7797768{col 76}{space 2} .2651199{col 87}{space 1}   -2.94{col 96}{space 3}0.003{col 104}{space 4}-1.300401{col 117}{space 3}-.2591529
{txt}{space 2}(Variant 3) S4.3. Providing resources to care for displaced  {c |}{col 64}{res}{space 2}-1.591592{col 76}{space 2} .3071587{col 87}{space 1}   -5.18{col 96}{space 3}0.000{col 104}{space 4}-2.194769{col 117}{space 3}-.9884153
{txt}{space 62} {c |}
{space 49}1.agreenudge2 {c |}{col 64}{res}{space 2}-1.002175{col 76}{space 2} .3124961{col 87}{space 1}   -3.21{col 96}{space 3}0.001{col 104}{space 4}-1.615833{col 117}{space 3} -.388517
{txt}{space 62} {c |}
{space 36}aidmediatortxt#agreenudge2 {c |}
(Variant 1) S4.1. Providing resources to care for displaced#1  {c |}{col 64}{res}{space 2} .6726732{col 76}{space 2} .4793231{col 87}{space 1}    1.40{col 96}{space 3}0.161{col 104}{space 4}-.2685884{col 117}{space 3} 1.613935
{txt}(Variant 3) S4.3. Providing resources to care for displaced#1  {c |}{col 64}{res}{space 2} 2.425531{col 76}{space 2} .4350637{col 87}{space 1}    5.58{col 96}{space 3}0.000{col 104}{space 4} 1.571183{col 117}{space 3} 3.279878
{txt}{space 62} {c |}
{space 57}_cons {c |}{col 64}{res}{space 2} 7.023879{col 76}{space 2} .2404897{col 87}{space 1}   29.21{col 96}{space 3}0.000{col 104}{space 4} 6.551622{col 117}{space 3} 7.496136
{txt}{hline 63}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins ib2.aidmediatortxt#agreenudge2
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:639}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 63}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 64}{c |}{col 76} Delta-method
{col 64}{c |}     Margin{col 76}   std. err.{col 88}      t{col 96}   P>|t|{col 104}     [95% con{col 117}f. interval]
{hline 63}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 36}aidmediatortxt#agreenudge2 {c |}
(Variant 1) S4.1. Providing resources to care for displaced#0  {c |}{col 64}{res}{space 2} 6.161622{col 76}{space 2} .1406134{col 87}{space 1}   43.82{col 96}{space 3}0.000{col 104}{space 4} 5.885495{col 117}{space 3} 6.437749
{txt}(Variant 1) S4.1. Providing resources to care for displaced#1  {c |}{col 64}{res}{space 2}  5.83212{col 76}{space 2} .3334568{col 87}{space 1}   17.49{col 96}{space 3}0.000{col 104}{space 4}   5.1773{col 117}{space 3} 6.486939
{txt}(Variant 2) S4.2. Providing resources to care for displaced#0  {c |}{col 64}{res}{space 2} 6.941398{col 76}{space 2} .2238718{col 87}{space 1}   31.01{col 96}{space 3}0.000{col 104}{space 4} 6.501775{col 117}{space 3} 7.381022
{txt}(Variant 2) S4.2. Providing resources to care for displaced#1  {c |}{col 64}{res}{space 2} 5.939223{col 76}{space 2} .2181068{col 87}{space 1}   27.23{col 96}{space 3}0.000{col 104}{space 4}  5.51092{col 117}{space 3} 6.367526
{txt}(Variant 3) S4.3. Providing resources to care for displaced#0  {c |}{col 64}{res}{space 2} 5.349806{col 76}{space 2} .2128515{col 87}{space 1}   25.13{col 96}{space 3}0.000{col 104}{space 4} 4.931823{col 117}{space 3} 5.767789
{txt}(Variant 3) S4.3. Providing resources to care for displaced#1  {c |}{col 64}{res}{space 2} 6.773162{col 76}{space 2} .2200593{col 87}{space 1}   30.78{col 96}{space 3}0.000{col 104}{space 4} 6.341025{col 117}{space 3} 7.205299
{txt}{hline 63}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot 
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:aidmediatortxt agreenudge2}{p_end}
{res}{txt}
{com}. 
. *Note additional formatting required
. 
. *Experiment 3 
. 
. *Experiment 3 Balance Tests
. 
. iebaltab ownpets dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income employment urban_rural region4, groupvar(humandeathtxt) savexlsx(balance3)

{res}{phang}Balance table saved in Excel format to: {browse "balance3.xlsx":balance3.xlsx}{p_end}
{txt}
{com}. 
. *Experiment 3 Robustness Checks
. 
. oprobit punishment humandeathtxt, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-2844.7144}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-2824.3286}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-2824.3273}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-2824.3273}  
{res}
{txt}{col 1}Ordered probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,746}
{txt}{col 57}{lalign 13:Wald chi2({res:1})}{col 70} = {res}{ralign 6:40.12}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-2824.3273}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0072}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}   punishment{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humandeathtxt {c |}{col 15}{res}{space 2} .3202402{col 27}{space 2} .0505585{col 38}{space 1}    6.33{col 47}{space 3}0.000{col 55}{space 4} .2211473{col 68}{space 3} .4193331
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2} -1.83233{col 27}{space 2} .0674917{col 55}{space 4}-1.964611{col 68}{space 3}-1.700049
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2}-.4712749{col 27}{space 2} .0414884{col 55}{space 4}-.5525907{col 68}{space 3}-.3899591
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2} .3452812{col 27}{space 2} .0414092{col 55}{space 4} .2641206{col 68}{space 3} .4264417
{txt}{space 8}/cut4 {c |}{col 15}{res}{space 2} .7596381{col 27}{space 2}  .043407{col 55}{space 4} .6745619{col 68}{space 3} .8447143
{txt}{space 8}/cut5 {c |}{col 15}{res}{space 2} 1.329518{col 27}{space 2} .0493208{col 55}{space 4} 1.232851{col 68}{space 3} 1.426185
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. oprobit punishment humandeathtxt  i.ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4 , robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-2749.3891}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-2671.8912}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-2671.8477}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-2671.8476}  
{res}
{txt}{col 1}Ordered probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,693}
{txt}{col 57}{lalign 13:Wald chi2({res:29})}{col 70} = {res}{ralign 6:160.05}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-2671.8476}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0282}

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                             punishment{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      z{col 73}   P>|z|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}humandeathtxt {c |}{col 41}{res}{space 2} .3476012{col 53}{space 2} .0515346{col 64}{space 1}    6.75{col 73}{space 3}0.000{col 81}{space 4} .2465953{col 94}{space 3}  .448607
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2} .1194302{col 53}{space 2} .0578745{col 64}{space 1}    2.06{col 73}{space 3}0.039{col 81}{space 4} .0059982{col 94}{space 3} .2328622
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2}  .132604{col 53}{space 2} .1826301{col 64}{space 1}    0.73{col 73}{space 3}0.468{col 81}{space 4}-.2253444{col 94}{space 3} .4905524
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2}-.0110393{col 53}{space 2} .0903039{col 64}{space 1}   -0.12{col 73}{space 3}0.903{col 81}{space 4}-.1880317{col 94}{space 3}  .165953
{txt}{space 39} {c |}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2} .1130017{col 53}{space 2} .0686591{col 64}{space 1}    1.65{col 73}{space 3}0.100{col 81}{space 4}-.0215677{col 94}{space 3} .2475712
{txt}{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2}-.0791619{col 53}{space 2} .0921156{col 64}{space 1}   -0.86{col 73}{space 3}0.390{col 81}{space 4}-.2597052{col 94}{space 3} .1013814
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2} .1708772{col 53}{space 2} .0515436{col 64}{space 1}    3.32{col 73}{space 3}0.001{col 81}{space 4} .0698537{col 94}{space 3} .2719007
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2}-.0452909{col 53}{space 2} .0782335{col 64}{space 1}   -0.58{col 73}{space 3}0.563{col 81}{space 4}-.1986257{col 94}{space 3} .1080439
{txt}{space 30}displaced {c |}{col 41}{res}{space 2}-.0087607{col 53}{space 2} .0779598{col 64}{space 1}   -0.11{col 73}{space 3}0.911{col 81}{space 4}-.1615591{col 94}{space 3} .1440378
{txt}{space 30}ukrainian {c |}{col 41}{res}{space 2} .0959432{col 53}{space 2} .1320501{col 64}{space 1}    0.73{col 73}{space 3}0.467{col 81}{space 4}-.1628702{col 94}{space 3} .3547566
{txt}{space 32}russian {c |}{col 41}{res}{space 2}-.2567488{col 53}{space 2}  .204566{col 64}{space 1}   -1.26{col 73}{space 3}0.209{col 81}{space 4}-.6576909{col 94}{space 3} .1441932
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2}-.4494822{col 53}{space 2} .0916573{col 64}{space 1}   -4.90{col 73}{space 3}0.000{col 81}{space 4}-.6291273{col 94}{space 3}-.2698372
{txt}{space 33}female {c |}{col 41}{res}{space 2}-.0242736{col 53}{space 2} .0561669{col 64}{space 1}   -0.43{col 73}{space 3}0.666{col 81}{space 4}-.1343587{col 94}{space 3} .0858116
{txt}{space 36}age {c |}{col 41}{res}{space 2} .0016148{col 53}{space 2} .0023046{col 64}{space 1}    0.70{col 73}{space 3}0.484{col 81}{space 4}-.0029021{col 94}{space 3} .0061316
{txt}{space 30}education {c |}{col 41}{res}{space 2}-.0858495{col 53}{space 2}  .020313{col 64}{space 1}   -4.23{col 73}{space 3}0.000{col 81}{space 4}-.1256622{col 94}{space 3}-.0460368
{txt}{space 33}income {c |}{col 41}{res}{space 2}-.0483931{col 53}{space 2}  .034359{col 64}{space 1}   -1.41{col 73}{space 3}0.159{col 81}{space 4}-.1157355{col 94}{space 3} .0189492
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2} -.070845{col 53}{space 2} .1303389{col 64}{space 1}   -0.54{col 73}{space 3}0.587{col 81}{space 4}-.3263045{col 94}{space 3} .1846144
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2}-.1497187{col 53}{space 2} .1034039{col 64}{space 1}   -1.45{col 73}{space 3}0.148{col 81}{space 4}-.3523867{col 94}{space 3} .0529493
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2}-.2898453{col 53}{space 2} .1358914{col 64}{space 1}   -2.13{col 73}{space 3}0.033{col 81}{space 4}-.5561876{col 94}{space 3} -.023503
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2}-.4080768{col 53}{space 2} .1335683{col 64}{space 1}   -3.06{col 73}{space 3}0.002{col 81}{space 4}-.6698658{col 94}{space 3}-.1462879
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2} .1528621{col 53}{space 2} .1805246{col 64}{space 1}    0.85{col 73}{space 3}0.397{col 81}{space 4}-.2009597{col 94}{space 3} .5066839
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2} -.160887{col 53}{space 2} .1373843{col 64}{space 1}   -1.17{col 73}{space 3}0.242{col 81}{space 4}-.4301553{col 94}{space 3} .1083813
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2}-.0850705{col 53}{space 2} .1116158{col 64}{space 1}   -0.76{col 73}{space 3}0.446{col 81}{space 4}-.3038334{col 94}{space 3} .1336923
{txt}{space 31}Student  {c |}{col 41}{res}{space 2}-.0855453{col 53}{space 2} .2123772{col 64}{space 1}   -0.40{col 73}{space 3}0.687{col 81}{space 4}-.5017969{col 94}{space 3} .3307063
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2} .0627799{col 53}{space 2} .1737011{col 64}{space 1}    0.36{col 73}{space 3}0.718{col 81}{space 4}-.2776681{col 94}{space 3} .4032278
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2}-.0731883{col 53}{space 2} .0815618{col 64}{space 1}   -0.90{col 73}{space 3}0.370{col 81}{space 4}-.2330464{col 94}{space 3} .0866698
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2} .0557448{col 53}{space 2} .1011037{col 64}{space 1}    0.55{col 73}{space 3}0.581{col 81}{space 4}-.1424149{col 94}{space 3} .2539044
{txt}{space 31}Central  {c |}{col 41}{res}{space 2}-.0238624{col 53}{space 2} .0875081{col 64}{space 1}   -0.27{col 73}{space 3}0.785{col 81}{space 4}-.1953752{col 94}{space 3} .1476503
{txt}{space 33}South  {c |}{col 41}{res}{space 2}-.1061237{col 53}{space 2}  .090414{col 64}{space 1}   -1.17{col 73}{space 3}0.240{col 81}{space 4}-.2833319{col 94}{space 3} .0710845
{txt}{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 34}/cut1 {c |}{col 41}{res}{space 2}-2.618195{col 53}{space 2} .3130034{col 81}{space 4} -3.23167{col 94}{space 3} -2.00472
{txt}{space 34}/cut2 {c |}{col 41}{res}{space 2}-1.180226{col 53}{space 2} .3061872{col 81}{space 4}-1.780341{col 94}{space 3}-.5801098
{txt}{space 34}/cut3 {c |}{col 41}{res}{space 2} -.324423{col 53}{space 2} .3047612{col 81}{space 4}-.9217439{col 94}{space 3}  .272898
{txt}{space 34}/cut4 {c |}{col 41}{res}{space 2} .1203898{col 53}{space 2} .3039326{col 81}{space 4}-.4753072{col 94}{space 3} .7160868
{txt}{space 34}/cut5 {c |}{col 41}{res}{space 2} .7140731{col 53}{space 2}  .304219{col 81}{space 4} .1178148{col 94}{space 3} 1.310331
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Experiment 3 Power Calculations
. 
. power oneway, n1(990) n2(1018) power(0.80 0.90 0.95 0.99)
{res}
{txt}Performing iteration ...
{res}
{p 0 2 2}{txt}Estimated{txt} between-group variance{txt} for one-way ANOVA{p_end}{txt}F test for group effect
{txt}{txt}{bind:H0: delta = 0}  {txt}versus  {bind:Ha: delta != 0}

  {txt}{c TLC}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c TRC}
  {txt}{c |}{txt}{txt}{ralign 8:alpha}{txt}{txt}{ralign 8:power}{txt}{txt}{ralign 8:N}{txt}{txt}{ralign 8:N_avg}{txt}{txt}{ralign 8:N1}{txt}{txt}{ralign 8:N2}{txt}{txt}{ralign 8:delta}{txt}{txt}{ralign 8:N_g}{txt}{txt}{ralign 8:Var_m}{txt}{txt}{ralign 8:Var_e}{txt}{txt} {c |}
  {txt}{c LT}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c RT}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.8}{res}{ralign 8:2,008}{res}{ralign 8:1004}{res}{ralign 8:990}{res}{ralign 8:1,018}{res}{ralign 8:.06255}{res}{ralign 8:2}{res}{ralign 8:.00391}{res}{ralign 8:1}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.9}{res}{ralign 8:2,008}{res}{ralign 8:1004}{res}{ralign 8:990}{res}{ralign 8:1,018}{res}{ralign 8:.07237}{res}{ralign 8:2}{res}{ralign 8:.00524}{res}{ralign 8:1}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.95}{res}{ralign 8:2,008}{res}{ralign 8:1004}{res}{ralign 8:990}{res}{ralign 8:1,018}{res}{ralign 8:.08048}{res}{ralign 8:2}{res}{ralign 8:.00648}{res}{ralign 8:1}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.99}{res}{ralign 8:2,008}{res}{ralign 8:1004}{res}{ralign 8:990}{res}{ralign 8:1,018}{res}{ralign 8:.0957}{res}{ralign 8:2}{res}{ralign 8:.00916}{res}{ralign 8:1}{txt} {c |}
  {txt}{c BLC}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c BRC}

{com}. esize twosample punishment, by(humandeathtxt)

{txt}Effect size based on mean comparison

                               Obs per group:
                            humandeathtxt==0 =        861
                            humandeathtxt==1 =        885
{res}{col 1}{text}{hline 20}{c TT}{hline 12}{hline 12}{hline 12}
{col 1}{text}        Effect size{col 21}{c |}   Estimate{col 34}    [95% conf. interval]
{res}{col 1}{text}{hline 20}{c +}{hline 12}{hline 12}{hline 12}
{col 1}{text}          Cohen's {it:d}{col 21}{c |}{result}{space 2}-.2922119{col 34}{space 3}-.3864906{col 46}{space 3}-.1978503
{col 1}{text}         Hedges's {it:g}{col 21}{c |}{result}{space 2}-.2920862{col 34}{space 3}-.3863243{col 46}{space 3}-.1977652
{col 1}{text}{hline 20}{c BT}{hline 12}{hline 12}{hline 12}
{res}{txt}
{com}. 
. *Sensitivity Analysis – Experiment 3 and Correlates of Punishment
. 
. reg punishment humandeathtxt, robust

{txt}Linear regression                               Number of obs     = {res}     1,746
                                                {txt}F(1, 1744)        =  {res}    37.24
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0209
                                                {txt}Root MSE          =    {res} 1.3762

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}   punishment{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humandeathtxt {c |}{col 15}{res}{space 2} .4021339{col 27}{space 2} .0658938{col 38}{space 1}    6.10{col 47}{space 3}0.000{col 55}{space 4} .2728948{col 68}{space 3}  .531373
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 3.337979{col 27}{space 2} .0473855{col 38}{space 1}   70.44{col 47}{space 3}0.000{col 55}{space 4} 3.245041{col 68}{space 3} 3.430918
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg punishment dinjured, robust

{txt}Linear regression                               Number of obs     = {res}     1,746
                                                {txt}F(1, 1744)        =  {res}    12.75
                                                {txt}Prob > F          = {res}    0.0004
                                                {txt}R-squared         = {res}    0.0073
                                                {txt}Root MSE          =    {res} 1.3857

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  punishment{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}dinjured {c |}{col 14}{res}{space 2} .2370606{col 26}{space 2} .0663835{col 37}{space 1}    3.57{col 46}{space 3}0.000{col 54}{space 4} .1068609{col 67}{space 3} .3672603
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.427624{col 26}{space 2} .0459526{col 37}{space 1}   74.59{col 46}{space 3}0.000{col 54}{space 4} 3.337496{col 67}{space 3} 3.517752
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg punishment russpeaker, robust

{txt}Linear regression                               Number of obs     = {res}     1,746
                                                {txt}F(1, 1744)        =  {res}    31.81
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0161
                                                {txt}Root MSE          =    {res} 1.3796

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  punishment{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}russpeaker {c |}{col 14}{res}{space 2}-.5585978{col 26}{space 2} .0990424{col 37}{space 1}   -5.64{col 46}{space 3}0.000{col 54}{space 4}-.7528521{col 67}{space 3}-.3643435
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.604516{col 26}{space 2} .0353016{col 37}{space 1}  102.11{col 46}{space 3}0.000{col 54}{space 4} 3.535278{col 67}{space 3} 3.673754
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg punishment education, robust

{txt}Linear regression                               Number of obs     = {res}     1,745
                                                {txt}F(1, 1743)        =  {res}    35.98
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0210
                                                {txt}Root MSE          =    {res}  1.376

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  punishment{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}education {c |}{col 14}{res}{space 2} -.139452{col 26}{space 2} .0232486{col 37}{space 1}   -6.00{col 46}{space 3}0.000{col 54}{space 4}-.1850501{col 67}{space 3}-.0938539
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 4.470349{col 26}{space 2} .1610323{col 37}{space 1}   27.76{col 46}{space 3}0.000{col 54}{space 4} 4.154512{col 67}{space 3} 4.786186
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. regsensitivity bounds punishment humandeathtxt ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income employment region4, dmp robust
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: DMP (2022){col 48}{txt}Number of obs{col 67}{res}=       1,693
{col 48}{txt}Beta(short){col 67}{res}=       0.403
{txt}Treatment{col 18}{res}: humandeathtxt{col 48}{txt}Beta(medium){col 67}{res}=       0.398
{txt}Outcome{col 18}{res}: punishment{col 48}{txt}R2(short){col 67}{res}=       0.021
{col 48}{txt}R2(medium){col 67}{res}=       0.075
{col 48}{txt}Var(Y){col 67}{res}=       1.902
{col 48}{txt}Var(X){col 67}{res}=       0.250
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.248

{txt}Hypothesis{col 18}{res}: Beta > 0         {col 48}{txt}Breakdown point{col 67}{res}=        85.8%
{txt}Other Params{col 18}{res}: cbar = 1, rybar = +inf

{txt}{hline 80}
 rxbar{col 35} Beta
{hline 80}
{res}{col 2}0.000{col 35}{txt}[{res} 0.3980{txt}, {res} 0.3980{txt} ]
{col 2}{res}0.102{col 35}{txt}[{res} 0.3738{txt}, {res} 0.4222{txt} ]
{col 2}{res}0.205{col 35}{txt}[{res} 0.3488{txt}, {res} 0.4473{txt} ]
{col 2}{res}0.307{col 35}{txt}[{res} 0.3221{txt}, {res} 0.4740{txt} ]
{col 2}{res}0.409{col 35}{txt}[{res} 0.2923{txt}, {res} 0.5037{txt} ]
{col 2}{res}0.511{col 35}{txt}[{res} 0.2577{txt}, {res} 0.5384{txt} ]
{col 2}{res}0.614{col 35}{txt}[{res} 0.2145{txt}, {res} 0.5815{txt} ]
{col 2}{res}0.716{col 35}{txt}[{res} 0.1554{txt}, {res} 0.6406{txt} ]
{col 2}{res}0.818{col 35}{txt}[{res} 0.0600{txt}, {res} 0.7360{txt} ]
{col 2}{res}0.921{col 35}{txt}[{res}-0.1697{txt}, {res} 0.9658{txt} ]
{col 2}{res}0.996{col 35}{txt}[   {res}-inf{txt},    {res}+inf{txt} ]
{hline 80}

{com}. 
. regsensitivity bounds punishment humandeathtxt ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age education income employment region4, oster robust
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: Oster (2019){col 48}{txt}Number of obs{col 67}{res}=       1,693
{col 48}{txt}Beta(short){col 67}{res}=       0.403
{txt}Treatment{col 18}{res}: humandeathtxt{col 48}{txt}Beta(medium){col 67}{res}=       0.398
{txt}Outcome{col 18}{res}: punishment{col 48}{txt}R2(short){col 67}{res}=       0.021
{col 48}{txt}R2(medium){col 67}{res}=       0.075
{col 48}{txt}Var(Y){col 67}{res}=       1.902
{col 48}{txt}Var(X){col 67}{res}=       0.250
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.248

{txt}Hypothesis{col 18}{res}: Beta != 0         {col 48}{txt}Breakdown point{col 67}{res}=         286%
{txt}Other Params{col 18}{res}: R-squared(long) = 1

{txt}{hline 80}
 Delta{col 35} Beta
{hline 80}
{res}{col 2}-0.990{col 35}{txt}{{res} 0.4712{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.800{col 35}{txt}{{res} 0.4585{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.600{col 35}{txt}{{res} 0.4445{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.400{col 35}{txt}{{res} 0.4298{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.200{col 35}{txt}{{res} 0.4143{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.000{col 35}{txt}{{res} 0.3980{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.200{col 35}{txt}{{res} 0.3808{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.400{col 35}{txt}{{res} 0.3627{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.600{col 35}{txt}{{res} 0.3434{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.800{col 35}{txt}{{res} 0.3230{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.990{col 35}{txt}{{res} 0.3024{txt}, {res}      .{txt}, {res}      .{txt} }
{hline 80}

{com}. 
. regsensitivity bounds punishment dinjured humandeathtxt ownpets  dsawviolencehuman dsawviolenceanimals  dlostanimals displaced ukrainian russian russpeaker female age education income employment region4, dmp robust
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: DMP (2022){col 48}{txt}Number of obs{col 67}{res}=       1,693
{col 48}{txt}Beta(short){col 67}{res}=       0.227
{txt}Treatment{col 18}{res}: dinjured{col 48}{txt}Beta(medium){col 67}{res}=       0.220
{txt}Outcome{col 18}{res}: punishment{col 48}{txt}R2(short){col 67}{res}=       0.007
{col 48}{txt}R2(medium){col 67}{res}=       0.075
{col 48}{txt}Var(Y){col 67}{res}=       1.902
{col 48}{txt}Var(X){col 67}{res}=       0.250
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.241

{txt}Hypothesis{col 18}{res}: Beta > 0         {col 48}{txt}Breakdown point{col 67}{res}=        38.1%
{txt}Other Params{col 18}{res}: cbar = 1, rybar = +inf

{txt}{hline 80}
 rxbar{col 35} Beta
{hline 80}
{res}{col 2}0.000{col 35}{txt}[{res} 0.2201{txt}, {res} 0.2201{txt} ]
{col 2}{res}0.099{col 35}{txt}[{res} 0.1672{txt}, {res} 0.2730{txt} ]
{col 2}{res}0.198{col 35}{txt}[{res} 0.1126{txt}, {res} 0.3276{txt} ]
{col 2}{res}0.297{col 35}{txt}[{res} 0.0544{txt}, {res} 0.3858{txt} ]
{col 2}{res}0.395{col 35}{txt}[{res}-0.0100{txt}, {res} 0.4503{txt} ]
{col 2}{res}0.494{col 35}{txt}[{res}-0.0847{txt}, {res} 0.5249{txt} ]
{col 2}{res}0.593{col 35}{txt}[{res}-0.1765{txt}, {res} 0.6167{txt} ]
{col 2}{res}0.692{col 35}{txt}[{res}-0.2999{txt}, {res} 0.7401{txt} ]
{col 2}{res}0.791{col 35}{txt}[{res}-0.4917{txt}, {res} 0.9320{txt} ]
{col 2}{res}0.890{col 35}{txt}[{res}-0.9045{txt}, {res} 1.3447{txt} ]
{col 2}{res}0.981{col 35}{txt}[   {res}-inf{txt},    {res}+inf{txt} ]
{hline 80}

{com}. 
. regsensitivity bounds punishment dinjured humandeathtxt ownpets  dsawviolencehuman dsawviolenceanimals  dlostanimals displaced ukrainian russian russpeaker female age education income employment region4, oster robust
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: Oster (2019){col 48}{txt}Number of obs{col 67}{res}=       1,693
{col 48}{txt}Beta(short){col 67}{res}=       0.227
{txt}Treatment{col 18}{res}: dinjured{col 48}{txt}Beta(medium){col 67}{res}=       0.220
{txt}Outcome{col 18}{res}: punishment{col 48}{txt}R2(short){col 67}{res}=       0.007
{col 48}{txt}R2(medium){col 67}{res}=       0.075
{col 48}{txt}Var(Y){col 67}{res}=       1.902
{col 48}{txt}Var(X){col 67}{res}=       0.250
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.241

{txt}Hypothesis{col 18}{res}: Beta != 0         {col 48}{txt}Breakdown point{col 67}{res}=         103%
{txt}Other Params{col 18}{res}: R-squared(long) = 1

{txt}{hline 80}
 Delta{col 35} Beta
{hline 80}
{res}{col 2}-0.990{col 35}{txt}{{res} 0.2843{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.800{col 35}{txt}{{res} 0.2756{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.600{col 35}{txt}{{res} 0.2651{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.400{col 35}{txt}{{res} 0.2527{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.200{col 35}{txt}{{res} 0.2380{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.000{col 35}{txt}{{res} 0.2201{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.200{col 35}{txt}{{res} 0.1981{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.400{col 35}{txt}{{res} 0.1701{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.600{col 35}{txt}{{res} 0.1336{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.800{col 35}{txt}{{res} 0.0838{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.990{col 35}{txt}{{res} 0.0161{txt}, {res}      .{txt}, {res}      .{txt} }
{hline 80}

{com}. 
. regsensitivity bounds punishment russpeaker humandeathtxt ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian female age education income employment region4, dmp robust
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: DMP (2022){col 48}{txt}Number of obs{col 67}{res}=       1,693
{col 48}{txt}Beta(short){col 67}{res}=      -0.579
{txt}Treatment{col 18}{res}: russpeaker{col 48}{txt}Beta(medium){col 67}{res}=      -0.531
{txt}Outcome{col 18}{res}: punishment{col 48}{txt}R2(short){col 67}{res}=       0.017
{col 48}{txt}R2(medium){col 67}{res}=       0.075
{col 48}{txt}Var(Y){col 67}{res}=       1.902
{col 48}{txt}Var(X){col 67}{res}=       0.097
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.079

{txt}Hypothesis{col 18}{res}: Beta < 0         {col 48}{txt}Breakdown point{col 67}{res}=        22.8%
{txt}Other Params{col 18}{res}: cbar = 1, rybar = +inf

{txt}{hline 80}
 rxbar{col 35} Beta
{hline 80}
{res}{col 2}0.000{col 35}{txt}[{res}-0.5305{txt}, {res}-0.5305{txt} ]
{col 2}{res}0.091{col 35}{txt}[{res}-0.7364{txt}, {res}-0.3246{txt} ]
{col 2}{res}0.182{col 35}{txt}[{res}-0.9489{txt}, {res}-0.1121{txt} ]
{col 2}{res}0.273{col 35}{txt}[{res}-1.1754{txt}, {res} 0.1144{txt} ]
{col 2}{res}0.364{col 35}{txt}[{res}-1.4261{txt}, {res} 0.3651{txt} ]
{col 2}{res}0.455{col 35}{txt}[{res}-1.7167{txt}, {res} 0.6557{txt} ]
{col 2}{res}0.546{col 35}{txt}[{res}-2.0744{txt}, {res} 1.0134{txt} ]
{col 2}{res}0.637{col 35}{txt}[{res}-2.5550{txt}, {res} 1.4940{txt} ]
{col 2}{res}0.728{col 35}{txt}[{res}-3.3036{txt}, {res} 2.2426{txt} ]
{col 2}{res}0.819{col 35}{txt}[{res}-4.9194{txt}, {res} 3.8583{txt} ]
{col 2}{res}0.902{col 35}{txt}[   {res}-inf{txt},    {res}+inf{txt} ]
{hline 80}

{com}. 
. regsensitivity bounds punishment russpeaker humandeathtxt ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian female age education income employment region4, oster robust
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: Oster (2019){col 48}{txt}Number of obs{col 67}{res}=       1,693
{col 48}{txt}Beta(short){col 67}{res}=      -0.579
{txt}Treatment{col 18}{res}: russpeaker{col 48}{txt}Beta(medium){col 67}{res}=      -0.531
{txt}Outcome{col 18}{res}: punishment{col 48}{txt}R2(short){col 67}{res}=       0.017
{col 48}{txt}R2(medium){col 67}{res}=       0.075
{col 48}{txt}Var(Y){col 67}{res}=       1.902
{col 48}{txt}Var(X){col 67}{res}=       0.097
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.079

{txt}Hypothesis{col 18}{res}: Beta != 0         {col 48}{txt}Breakdown point{col 67}{res}=        19.7%
{txt}Other Params{col 18}{res}: R-squared(long) = 1

{txt}{hline 80}
 Delta{col 35} Beta
{hline 80}
{res}{col 2}-0.990{col 35}{txt}{{res} -0.73{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}-0.800{col 35}{txt}{{res} -0.72{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}-0.600{col 35}{txt}{{res} -0.71{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}-0.400{col 35}{txt}{{res} -0.68{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}-0.200{col 35}{txt}{{res} -0.64{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}0.000{col 35}{txt}{{res} -0.53{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}0.200{col 35}{txt}{{res}  0.02{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}0.400{col 35}{txt}{{res} -2.61{txt}, {res} -1.36{txt}, {res}  1.69{txt} }
{col 2}{res}0.600{col 35}{txt}{{res} -5.04{txt}, {res} -1.00{txt}, {res}  3.55{txt} }
{col 2}{res}0.800{col 35}{txt}{{res} -8.82{txt}, {res} -0.92{txt}, {res}  6.59{txt} }
{col 2}{res}0.990{col 35}{txt}{{res}-55.44{txt}, {res} -0.88{txt}, {res} 28.66{txt} }
{hline 80}

{com}. 
. regsensitivity bounds punishment education humandeathtxt ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age  income employment region4, dmp robust
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: DMP (2022){col 48}{txt}Number of obs{col 67}{res}=       1,693
{col 48}{txt}Beta(short){col 67}{res}=      -0.137
{txt}Treatment{col 18}{res}: education{col 48}{txt}Beta(medium){col 67}{res}=      -0.126
{txt}Outcome{col 18}{res}: punishment{col 48}{txt}R2(short){col 67}{res}=       0.020
{col 48}{txt}R2(medium){col 67}{res}=       0.075
{col 48}{txt}Var(Y){col 67}{res}=       1.902
{col 48}{txt}Var(X){col 67}{res}=       2.050
{col 48}{txt}Var(X_Residual){col 67}{res}=       1.839

{txt}Hypothesis{col 18}{res}: Beta < 0         {col 48}{txt}Breakdown point{col 67}{res}=        35.4%
{txt}Other Params{col 18}{res}: cbar = 1, rybar = +inf

{txt}{hline 80}
 rxbar{col 35} Beta
{hline 80}
{res}{col 2}0.000{col 35}{txt}[{res}-0.1262{txt}, {res}-0.1262{txt} ]
{col 2}{res}0.095{col 35}{txt}[{res}-0.1577{txt}, {res}-0.0947{txt} ]
{col 2}{res}0.189{col 35}{txt}[{res}-0.1902{txt}, {res}-0.0622{txt} ]
{col 2}{res}0.284{col 35}{txt}[{res}-0.2248{txt}, {res}-0.0276{txt} ]
{col 2}{res}0.379{col 35}{txt}[{res}-0.2631{txt}, {res} 0.0106{txt} ]
{col 2}{res}0.474{col 35}{txt}[{res}-0.3073{txt}, {res} 0.0548{txt} ]
{col 2}{res}0.568{col 35}{txt}[{res}-0.3614{txt}, {res} 0.1090{txt} ]
{col 2}{res}0.663{col 35}{txt}[{res}-0.4336{txt}, {res} 0.1811{txt} ]
{col 2}{res}0.758{col 35}{txt}[{res}-0.5443{txt}, {res} 0.2919{txt} ]
{col 2}{res}0.853{col 35}{txt}[{res}-0.7738{txt}, {res} 0.5213{txt} ]
{col 2}{res}0.947{col 35}{txt}[   {res}-inf{txt},    {res}+inf{txt} ]
{hline 80}

{com}. 
. regsensitivity bounds punishment education humandeathtxt ownpets  dsawviolencehuman dsawviolenceanimals dinjured dlostanimals displaced ukrainian russian russpeaker female age income employment region4, oster robust
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: Oster (2019){col 48}{txt}Number of obs{col 67}{res}=       1,693
{col 48}{txt}Beta(short){col 67}{res}=      -0.137
{txt}Treatment{col 18}{res}: education{col 48}{txt}Beta(medium){col 67}{res}=      -0.126
{txt}Outcome{col 18}{res}: punishment{col 48}{txt}R2(short){col 67}{res}=       0.020
{col 48}{txt}R2(medium){col 67}{res}=       0.075
{col 48}{txt}Var(Y){col 67}{res}=       1.902
{col 48}{txt}Var(X){col 67}{res}=       2.050
{col 48}{txt}Var(X_Residual){col 67}{res}=       1.839

{txt}Hypothesis{col 18}{res}: Beta != 0         {col 48}{txt}Breakdown point{col 67}{res}=        30.2%
{txt}Other Params{col 18}{res}: R-squared(long) = 1

{txt}{hline 80}
 Delta{col 35} Beta
{hline 80}
{res}{col 2}-0.990{col 35}{txt}{{res} -0.19{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}-0.800{col 35}{txt}{{res} -0.19{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}-0.600{col 35}{txt}{{res} -0.18{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}-0.400{col 35}{txt}{{res} -0.17{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}-0.200{col 35}{txt}{{res} -0.16{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}0.000{col 35}{txt}{{res} -0.13{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}0.200{col 35}{txt}{{res} -0.06{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}0.400{col 35}{txt}{{res}  0.10{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}0.600{col 35}{txt}{{res}  0.41{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}0.800{col 35}{txt}{{res} -1.52{txt}, {res} -0.40{txt}, {res}  0.92{txt} }
{col 2}{res}0.990{col 35}{txt}{{res}-13.81{txt}, {res} -0.33{txt}, {res}  3.35{txt} }
{hline 80}

{com}. 
. *Correlates of Victimization
. 
. logit dsawviolencehuman  dsawviolenceanimals dinjured dlostanimals displaced i.ownpets  ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res: -971.7612}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-841.23421}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-831.46267}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-831.40322}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-831.40321}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,926}
{txt}{col 57}{lalign 13:Wald chi2({res:27})}{col 70} = {res}{ralign 6:239.64}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-831.40321}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1444}

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                      dsawviolencehuman{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      z{col 73}   P>|z|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2} 1.573251{col 53}{space 2} .1815744{col 64}{space 1}    8.66{col 73}{space 3}0.000{col 81}{space 4} 1.217372{col 94}{space 3}  1.92913
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2} .4749584{col 53}{space 2} .1273581{col 64}{space 1}    3.73{col 73}{space 3}0.000{col 81}{space 4} .2253411{col 94}{space 3} .7245757
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2} .8166182{col 53}{space 2} .1602955{col 64}{space 1}    5.09{col 73}{space 3}0.000{col 81}{space 4} .5024448{col 94}{space 3} 1.130792
{txt}{space 30}displaced {c |}{col 41}{res}{space 2} .6251531{col 53}{space 2} .1718932{col 64}{space 1}    3.64{col 73}{space 3}0.000{col 81}{space 4} .2882485{col 94}{space 3} .9620577
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}-.0281352{col 53}{space 2} .1412874{col 64}{space 1}   -0.20{col 73}{space 3}0.842{col 81}{space 4}-.3050534{col 94}{space 3} .2487829
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2} .2538561{col 53}{space 2} .4331599{col 64}{space 1}    0.59{col 73}{space 3}0.558{col 81}{space 4}-.5951217{col 94}{space 3} 1.102834
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2}-.2387566{col 53}{space 2} .2126305{col 64}{space 1}   -1.12{col 73}{space 3}0.261{col 81}{space 4}-.6555047{col 94}{space 3} .1779914
{txt}{space 39} {c |}
{space 30}ukrainian {c |}{col 41}{res}{space 2} .3042567{col 53}{space 2} .3354056{col 64}{space 1}    0.91{col 73}{space 3}0.364{col 81}{space 4}-.3531261{col 94}{space 3} .9616395
{txt}{space 32}russian {c |}{col 41}{res}{space 2}-.0933646{col 53}{space 2} .5693056{col 64}{space 1}   -0.16{col 73}{space 3}0.870{col 81}{space 4}-1.209183{col 94}{space 3} 1.022454
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2} .2743491{col 53}{space 2} .1984633{col 64}{space 1}    1.38{col 73}{space 3}0.167{col 81}{space 4}-.1146318{col 94}{space 3} .6633301
{txt}{space 33}female {c |}{col 41}{res}{space 2}-.6376269{col 53}{space 2} .1339035{col 64}{space 1}   -4.76{col 73}{space 3}0.000{col 81}{space 4}-.9000729{col 94}{space 3}-.3751809
{txt}{space 36}age {c |}{col 41}{res}{space 2} -.019898{col 53}{space 2} .0056309{col 64}{space 1}   -3.53{col 73}{space 3}0.000{col 81}{space 4}-.0309344{col 94}{space 3}-.0088617
{txt}{space 30}education {c |}{col 41}{res}{space 2} .0673958{col 53}{space 2} .0520863{col 64}{space 1}    1.29{col 73}{space 3}0.196{col 81}{space 4}-.0346914{col 94}{space 3}  .169483
{txt}{space 33}income {c |}{col 41}{res}{space 2}-.1411563{col 53}{space 2} .0773836{col 64}{space 1}   -1.82{col 73}{space 3}0.068{col 81}{space 4}-.2928255{col 94}{space 3} .0105129
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2}  .353703{col 53}{space 2} .2971237{col 64}{space 1}    1.19{col 73}{space 3}0.234{col 81}{space 4}-.2286487{col 94}{space 3} .9360547
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2} .2016061{col 53}{space 2} .2348465{col 64}{space 1}    0.86{col 73}{space 3}0.391{col 81}{space 4}-.2586846{col 94}{space 3} .6618968
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2}-.0480407{col 53}{space 2} .3537226{col 64}{space 1}   -0.14{col 73}{space 3}0.892{col 81}{space 4}-.7413243{col 94}{space 3} .6452429
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2} .3731031{col 53}{space 2} .3032956{col 64}{space 1}    1.23{col 73}{space 3}0.219{col 81}{space 4}-.2213454{col 94}{space 3} .9675515
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2} .6781419{col 53}{space 2} .4238531{col 64}{space 1}    1.60{col 73}{space 3}0.110{col 81}{space 4}-.1525948{col 94}{space 3} 1.508879
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2} -.097419{col 53}{space 2} .3246535{col 64}{space 1}   -0.30{col 73}{space 3}0.764{col 81}{space 4}-.7337282{col 94}{space 3} .5388901
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2} .2075439{col 53}{space 2} .2577443{col 64}{space 1}    0.81{col 73}{space 3}0.421{col 81}{space 4}-.2976256{col 94}{space 3} .7127134
{txt}{space 31}Student  {c |}{col 41}{res}{space 2} .0813087{col 53}{space 2} .5224169{col 64}{space 1}    0.16{col 73}{space 3}0.876{col 81}{space 4}-.9426095{col 94}{space 3} 1.105227
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2} .1655141{col 53}{space 2} .3690789{col 64}{space 1}    0.45{col 73}{space 3}0.654{col 81}{space 4}-.5578673{col 94}{space 3} .8888956
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2}-.1989591{col 53}{space 2} .1867141{col 64}{space 1}   -1.07{col 73}{space 3}0.287{col 81}{space 4}-.5649121{col 94}{space 3} .1669939
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2} -.569618{col 53}{space 2}  .243581{col 64}{space 1}   -2.34{col 73}{space 3}0.019{col 81}{space 4}-1.047028{col 94}{space 3} -.092208
{txt}{space 31}Central  {c |}{col 41}{res}{space 2}-.3932149{col 53}{space 2} .1999162{col 64}{space 1}   -1.97{col 73}{space 3}0.049{col 81}{space 4}-.7850434{col 94}{space 3}-.0013864
{txt}{space 33}South  {c |}{col 41}{res}{space 2}-.1158755{col 53}{space 2} .2040848{col 64}{space 1}   -0.57{col 73}{space 3}0.570{col 81}{space 4}-.5158744{col 94}{space 3} .2841234
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2}-1.346906{col 53}{space 2} .7172789{col 64}{space 1}   -1.88{col 73}{space 3}0.060{col 81}{space 4}-2.752747{col 94}{space 3} .0589346
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. logit dsawviolenceanimals dsawviolencehuman dinjured dlostanimals displaced i.ownpets  ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-579.58187}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-533.69524}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-508.11788}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-507.52466}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-507.52362}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-507.52362}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,926}
{txt}{col 57}{lalign 13:Wald chi2({res:27})}{col 70} = {res}{ralign 6:161.12}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-507.52362}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1243}

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                    dsawviolenceanimals{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      z{col 73}   P>|z|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2} 1.572454{col 53}{space 2} .1827058{col 64}{space 1}    8.61{col 73}{space 3}0.000{col 81}{space 4} 1.214357{col 94}{space 3} 1.930551
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2} .1985712{col 53}{space 2} .1814399{col 64}{space 1}    1.09{col 73}{space 3}0.274{col 81}{space 4}-.1570444{col 94}{space 3} .5541868
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2} .1588554{col 53}{space 2} .2211543{col 64}{space 1}    0.72{col 73}{space 3}0.473{col 81}{space 4}-.2745991{col 94}{space 3} .5923099
{txt}{space 30}displaced {c |}{col 41}{res}{space 2}-.1104822{col 53}{space 2}  .249902{col 64}{space 1}   -0.44{col 73}{space 3}0.658{col 81}{space 4} -.600281{col 94}{space 3} .3793167
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}  .409401{col 53}{space 2} .2040488{col 64}{space 1}    2.01{col 73}{space 3}0.045{col 81}{space 4} .0094727{col 94}{space 3} .8093294
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2}-.2651937{col 53}{space 2} .8368679{col 64}{space 1}   -0.32{col 73}{space 3}0.751{col 81}{space 4}-1.905425{col 94}{space 3} 1.375037
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2} .5065944{col 53}{space 2} .2992522{col 64}{space 1}    1.69{col 73}{space 3}0.090{col 81}{space 4}-.0799292{col 94}{space 3} 1.093118
{txt}{space 39} {c |}
{space 30}ukrainian {c |}{col 41}{res}{space 2} .2221018{col 53}{space 2} .4930519{col 64}{space 1}    0.45{col 73}{space 3}0.652{col 81}{space 4}-.7442621{col 94}{space 3} 1.188466
{txt}{space 32}russian {c |}{col 41}{res}{space 2}-.4309777{col 53}{space 2}  .839288{col 64}{space 1}   -0.51{col 73}{space 3}0.608{col 81}{space 4}-2.075952{col 94}{space 3} 1.213997
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2}-.1683486{col 53}{space 2} .2915057{col 64}{space 1}   -0.58{col 73}{space 3}0.564{col 81}{space 4}-.7396893{col 94}{space 3} .4029922
{txt}{space 33}female {c |}{col 41}{res}{space 2}  .213469{col 53}{space 2} .1800394{col 64}{space 1}    1.19{col 73}{space 3}0.236{col 81}{space 4}-.1394017{col 94}{space 3} .5663398
{txt}{space 36}age {c |}{col 41}{res}{space 2} .0163763{col 53}{space 2} .0076888{col 64}{space 1}    2.13{col 73}{space 3}0.033{col 81}{space 4} .0013064{col 94}{space 3} .0314461
{txt}{space 30}education {c |}{col 41}{res}{space 2}-.0221772{col 53}{space 2} .0663106{col 64}{space 1}   -0.33{col 73}{space 3}0.738{col 81}{space 4}-.1521436{col 94}{space 3} .1077893
{txt}{space 33}income {c |}{col 41}{res}{space 2}-.0842122{col 53}{space 2} .1174242{col 64}{space 1}   -0.72{col 73}{space 3}0.473{col 81}{space 4}-.3143594{col 94}{space 3}  .145935
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2} .1286096{col 53}{space 2} .4118372{col 64}{space 1}    0.31{col 73}{space 3}0.755{col 81}{space 4}-.6785765{col 94}{space 3} .9357956
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2} .3661554{col 53}{space 2} .3324989{col 64}{space 1}    1.10{col 73}{space 3}0.271{col 81}{space 4}-.2855304{col 94}{space 3} 1.017841
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2}-.2133666{col 53}{space 2} .5961541{col 64}{space 1}   -0.36{col 73}{space 3}0.720{col 81}{space 4}-1.381807{col 94}{space 3} .9550741
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2} -.415888{col 53}{space 2} .5010417{col 64}{space 1}   -0.83{col 73}{space 3}0.407{col 81}{space 4}-1.397912{col 94}{space 3} .5661357
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2} 1.910376{col 53}{space 2} .4887687{col 64}{space 1}    3.91{col 73}{space 3}0.000{col 81}{space 4}  .952407{col 94}{space 3} 2.868345
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2}   .30886{col 53}{space 2} .3996014{col 64}{space 1}    0.77{col 73}{space 3}0.440{col 81}{space 4}-.4743443{col 94}{space 3} 1.092064
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2} .0229352{col 53}{space 2} .3216672{col 64}{space 1}    0.07{col 73}{space 3}0.943{col 81}{space 4} -.607521{col 94}{space 3} .6533914
{txt}{space 31}Student  {c |}{col 41}{res}{space 2}-.4251969{col 53}{space 2} 1.144774{col 64}{space 1}   -0.37{col 73}{space 3}0.710{col 81}{space 4}-2.668913{col 94}{space 3} 1.818519
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2} .6998087{col 53}{space 2} .4760457{col 64}{space 1}    1.47{col 73}{space 3}0.142{col 81}{space 4}-.2332238{col 94}{space 3} 1.632841
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2}-.3063959{col 53}{space 2} .2713645{col 64}{space 1}   -1.13{col 73}{space 3}0.259{col 81}{space 4}-.8382606{col 94}{space 3} .2254688
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2}-1.057156{col 53}{space 2} .3432328{col 64}{space 1}   -3.08{col 73}{space 3}0.002{col 81}{space 4} -1.72988{col 94}{space 3}-.3844322
{txt}{space 31}Central  {c |}{col 41}{res}{space 2}-.4655731{col 53}{space 2} .2554642{col 64}{space 1}   -1.82{col 73}{space 3}0.068{col 81}{space 4}-.9662737{col 94}{space 3} .0351274
{txt}{space 33}South  {c |}{col 41}{res}{space 2}-.4862922{col 53}{space 2}  .262059{col 64}{space 1}   -1.86{col 73}{space 3}0.064{col 81}{space 4}-.9999184{col 94}{space 3}  .027334
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2}-3.273387{col 53}{space 2} 1.023097{col 64}{space 1}   -3.20{col 73}{space 3}0.001{col 81}{space 4} -5.27862{col 94}{space 3}-1.268155
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. logit  dinjured  dsawviolencehuman dsawviolenceanimals   dlostanimals displaced i.ownpets ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-1334.3524}  
Iteration 1:{space 2}Log pseudolikelihood = {res: -1287.798}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-1287.6806}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-1287.6805}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,926}
{txt}{col 57}{lalign 13:Wald chi2({res:27})}{col 70} = {res}{ralign 6:83.86}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-1287.6805}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0350}

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                               dinjured{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      z{col 73}   P>|z|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2} .4526057{col 53}{space 2} .1262515{col 64}{space 1}    3.58{col 73}{space 3}0.000{col 81}{space 4} .2051573{col 94}{space 3} .7000542
{txt}{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2} .2009813{col 53}{space 2} .1761985{col 64}{space 1}    1.14{col 73}{space 3}0.254{col 81}{space 4}-.1443615{col 94}{space 3} .5463241
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2} .1616332{col 53}{space 2} .1372528{col 64}{space 1}    1.18{col 73}{space 3}0.239{col 81}{space 4}-.1073774{col 94}{space 3} .4306438
{txt}{space 30}displaced {c |}{col 41}{res}{space 2}-.0872721{col 53}{space 2} .1541607{col 64}{space 1}   -0.57{col 73}{space 3}0.571{col 81}{space 4}-.3894216{col 94}{space 3} .2148773
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2} .2435139{col 53}{space 2} .1091538{col 64}{space 1}    2.23{col 73}{space 3}0.026{col 81}{space 4} .0295764{col 94}{space 3} .4574514
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2}-.0144172{col 53}{space 2} .3504266{col 64}{space 1}   -0.04{col 73}{space 3}0.967{col 81}{space 4}-.7012408{col 94}{space 3} .6724063
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2} .5624826{col 53}{space 2} .1593074{col 64}{space 1}    3.53{col 73}{space 3}0.000{col 81}{space 4} .2502459{col 94}{space 3} .8747193
{txt}{space 39} {c |}
{space 30}ukrainian {c |}{col 41}{res}{space 2}  .115545{col 53}{space 2} .2482069{col 64}{space 1}    0.47{col 73}{space 3}0.642{col 81}{space 4}-.3709315{col 94}{space 3} .6020215
{txt}{space 32}russian {c |}{col 41}{res}{space 2} .4546922{col 53}{space 2} .3735049{col 64}{space 1}    1.22{col 73}{space 3}0.223{col 81}{space 4} -.277364{col 94}{space 3} 1.186748
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2}-.4230326{col 53}{space 2} .1645463{col 64}{space 1}   -2.57{col 73}{space 3}0.010{col 81}{space 4}-.7455374{col 94}{space 3}-.1005279
{txt}{space 33}female {c |}{col 41}{res}{space 2}-.0613271{col 53}{space 2} .1008172{col 64}{space 1}   -0.61{col 73}{space 3}0.543{col 81}{space 4}-.2589251{col 94}{space 3} .1362709
{txt}{space 36}age {c |}{col 41}{res}{space 2}-.0098677{col 53}{space 2} .0042659{col 64}{space 1}   -2.31{col 73}{space 3}0.021{col 81}{space 4}-.0182287{col 94}{space 3}-.0015067
{txt}{space 30}education {c |}{col 41}{res}{space 2}-.0097643{col 53}{space 2} .0361618{col 64}{space 1}   -0.27{col 73}{space 3}0.787{col 81}{space 4}-.0806402{col 94}{space 3} .0611115
{txt}{space 33}income {c |}{col 41}{res}{space 2}-.0248946{col 53}{space 2} .0588847{col 64}{space 1}   -0.42{col 73}{space 3}0.672{col 81}{space 4}-.1403066{col 94}{space 3} .0905173
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2}-.2773369{col 53}{space 2} .2239383{col 64}{space 1}   -1.24{col 73}{space 3}0.216{col 81}{space 4} -.716248{col 94}{space 3} .1615741
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2}-.0401792{col 53}{space 2}  .180217{col 64}{space 1}   -0.22{col 73}{space 3}0.824{col 81}{space 4}-.3933981{col 94}{space 3} .3130397
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2}-.3159825{col 53}{space 2} .2585186{col 64}{space 1}   -1.22{col 73}{space 3}0.222{col 81}{space 4}-.8226696{col 94}{space 3} .1907045
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2} .0724669{col 53}{space 2} .2400196{col 64}{space 1}    0.30{col 73}{space 3}0.763{col 81}{space 4}-.3979629{col 94}{space 3} .5428967
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2} .5818779{col 53}{space 2}  .413042{col 64}{space 1}    1.41{col 73}{space 3}0.159{col 81}{space 4}-.2276695{col 94}{space 3} 1.391425
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2}-.3215962{col 53}{space 2} .2284423{col 64}{space 1}   -1.41{col 73}{space 3}0.159{col 81}{space 4}-.7693349{col 94}{space 3} .1261424
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2}-.2109704{col 53}{space 2} .1818783{col 64}{space 1}   -1.16{col 73}{space 3}0.246{col 81}{space 4}-.5674452{col 94}{space 3} .1455044
{txt}{space 31}Student  {c |}{col 41}{res}{space 2}-.6366464{col 53}{space 2} .4632632{col 64}{space 1}   -1.37{col 73}{space 3}0.169{col 81}{space 4}-1.544626{col 94}{space 3} .2713329
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2} .2490905{col 53}{space 2} .2980443{col 64}{space 1}    0.84{col 73}{space 3}0.403{col 81}{space 4}-.3350657{col 94}{space 3} .8332467
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2} .0092789{col 53}{space 2}  .136817{col 64}{space 1}    0.07{col 73}{space 3}0.946{col 81}{space 4}-.2588774{col 94}{space 3} .2774352
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2}  -.05405{col 53}{space 2} .1879583{col 64}{space 1}   -0.29{col 73}{space 3}0.774{col 81}{space 4}-.4224415{col 94}{space 3} .3143415
{txt}{space 31}Central  {c |}{col 41}{res}{space 2}-.2762273{col 53}{space 2}  .162199{col 64}{space 1}   -1.70{col 73}{space 3}0.089{col 81}{space 4}-.5941315{col 94}{space 3}  .041677
{txt}{space 33}South  {c |}{col 41}{res}{space 2}-.0850712{col 53}{space 2} .1681608{col 64}{space 1}   -0.51{col 73}{space 3}0.613{col 81}{space 4}-.4146604{col 94}{space 3} .2445179
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2} .5846864{col 53}{space 2} .5480513{col 64}{space 1}    1.07{col 73}{space 3}0.286{col 81}{space 4}-.4894745{col 94}{space 3} 1.658847
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. logit dlostanimals dsawviolencehuman dsawviolenceanimals  dinjured displaced i.ownpets ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-807.34167}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-731.56926}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-720.64101}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-720.53522}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-720.53519}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,926}
{txt}{col 57}{lalign 13:Wald chi2({res:27})}{col 70} = {res}{ralign 6:160.20}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-720.53519}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1075}

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                           dlostanimals{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      z{col 73}   P>|z|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2} .8182989{col 53}{space 2} .1584552{col 64}{space 1}    5.16{col 73}{space 3}0.000{col 81}{space 4} .5077324{col 94}{space 3} 1.128865
{txt}{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2} .1881459{col 53}{space 2} .2198601{col 64}{space 1}    0.86{col 73}{space 3}0.392{col 81}{space 4}-.2427719{col 94}{space 3} .6190638
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2} .1582785{col 53}{space 2} .1378451{col 64}{space 1}    1.15{col 73}{space 3}0.251{col 81}{space 4}-.1118928{col 94}{space 3} .4284499
{txt}{space 30}displaced {c |}{col 41}{res}{space 2} .7006605{col 53}{space 2} .1781856{col 64}{space 1}    3.93{col 73}{space 3}0.000{col 81}{space 4} .3514231{col 94}{space 3} 1.049898
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2} .3531842{col 53}{space 2} .1618023{col 64}{space 1}    2.18{col 73}{space 3}0.029{col 81}{space 4} .0360575{col 94}{space 3} .6703109
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2} .3374513{col 53}{space 2} .5065314{col 64}{space 1}    0.67{col 73}{space 3}0.505{col 81}{space 4} -.655332{col 94}{space 3} 1.330235
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2}-.0753831{col 53}{space 2} .2518198{col 64}{space 1}   -0.30{col 73}{space 3}0.765{col 81}{space 4}-.5689407{col 94}{space 3} .4181746
{txt}{space 39} {c |}
{space 30}ukrainian {c |}{col 41}{res}{space 2}  .019799{col 53}{space 2}  .343994{col 64}{space 1}    0.06{col 73}{space 3}0.954{col 81}{space 4} -.654417{col 94}{space 3} .6940149
{txt}{space 32}russian {c |}{col 41}{res}{space 2}  .042922{col 53}{space 2}  .532583{col 64}{space 1}    0.08{col 73}{space 3}0.936{col 81}{space 4}-1.000921{col 94}{space 3} 1.086765
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2}-.1675212{col 53}{space 2} .2263664{col 64}{space 1}   -0.74{col 73}{space 3}0.459{col 81}{space 4}-.6111912{col 94}{space 3} .2761488
{txt}{space 33}female {c |}{col 41}{res}{space 2}-.3063775{col 53}{space 2} .1522471{col 64}{space 1}   -2.01{col 73}{space 3}0.044{col 81}{space 4}-.6047763{col 94}{space 3}-.0079787
{txt}{space 36}age {c |}{col 41}{res}{space 2}-.0035227{col 53}{space 2} .0062346{col 64}{space 1}   -0.57{col 73}{space 3}0.572{col 81}{space 4}-.0157422{col 94}{space 3} .0086968
{txt}{space 30}education {c |}{col 41}{res}{space 2}-.0444421{col 53}{space 2} .0533113{col 64}{space 1}   -0.83{col 73}{space 3}0.404{col 81}{space 4}-.1489302{col 94}{space 3} .0600461
{txt}{space 33}income {c |}{col 41}{res}{space 2}-.0131733{col 53}{space 2} .0885798{col 64}{space 1}   -0.15{col 73}{space 3}0.882{col 81}{space 4}-.1867864{col 94}{space 3} .1604398
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2} .1753965{col 53}{space 2}   .33223{col 64}{space 1}    0.53{col 73}{space 3}0.598{col 81}{space 4}-.4757623{col 94}{space 3} .8265553
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2} .3924839{col 53}{space 2} .2649984{col 64}{space 1}    1.48{col 73}{space 3}0.139{col 81}{space 4}-.1269034{col 94}{space 3} .9118712
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2}-.1024503{col 53}{space 2} .4087351{col 64}{space 1}   -0.25{col 73}{space 3}0.802{col 81}{space 4}-.9035564{col 94}{space 3} .6986558
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2} .7008425{col 53}{space 2} .3383497{col 64}{space 1}    2.07{col 73}{space 3}0.038{col 81}{space 4} .0376891{col 94}{space 3} 1.363996
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2}-.2588918{col 53}{space 2} .5420319{col 64}{space 1}   -0.48{col 73}{space 3}0.633{col 81}{space 4}-1.321255{col 94}{space 3} .8034711
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2}  .682722{col 53}{space 2} .3161216{col 64}{space 1}    2.16{col 73}{space 3}0.031{col 81}{space 4} .0631351{col 94}{space 3} 1.302309
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2}-.0343049{col 53}{space 2} .2821943{col 64}{space 1}   -0.12{col 73}{space 3}0.903{col 81}{space 4}-.5873955{col 94}{space 3} .5187857
{txt}{space 31}Student  {c |}{col 41}{res}{space 2}-.2461702{col 53}{space 2} .5965133{col 64}{space 1}   -0.41{col 73}{space 3}0.680{col 81}{space 4}-1.415315{col 94}{space 3} .9229744
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2} .4952753{col 53}{space 2} .3589062{col 64}{space 1}    1.38{col 73}{space 3}0.168{col 81}{space 4} -.208168{col 94}{space 3} 1.198719
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2} .3437985{col 53}{space 2} .1904914{col 64}{space 1}    1.80{col 73}{space 3}0.071{col 81}{space 4}-.0295578{col 94}{space 3} .7171548
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2}-1.765279{col 53}{space 2} .3063837{col 64}{space 1}   -5.76{col 73}{space 3}0.000{col 81}{space 4} -2.36578{col 94}{space 3}-1.164778
{txt}{space 31}Central  {c |}{col 41}{res}{space 2}-.6674278{col 53}{space 2} .2002539{col 64}{space 1}   -3.33{col 73}{space 3}0.001{col 81}{space 4}-1.059918{col 94}{space 3}-.2749374
{txt}{space 33}South  {c |}{col 41}{res}{space 2}-.5754618{col 53}{space 2} .2029228{col 64}{space 1}   -2.84{col 73}{space 3}0.005{col 81}{space 4}-.9731831{col 94}{space 3}-.1777404
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2}-2.355463{col 53}{space 2} .7667854{col 64}{space 1}   -3.07{col 73}{space 3}0.002{col 81}{space 4}-3.858335{col 94}{space 3}-.8525915
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. logit ddisplaced  dsawviolencehuman dsawviolenceanimals  dinjured dlostanimals i.ownpets  ukrainian russian russpeaker female age education income i.employment urban_rural ib4.region4, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-754.79214}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-636.94884}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-606.03006}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-605.59758}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-605.59644}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-605.59644}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,926}
{txt}{col 57}{lalign 13:Wald chi2({res:27})}{col 70} = {res}{ralign 6:226.66}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-605.59644}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1977}

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                             ddisplaced{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      z{col 73}   P>|z|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}dsawviolencehuman {c |}{col 41}{res}{space 2} .6308105{col 53}{space 2} .1698497{col 64}{space 1}    3.71{col 73}{space 3}0.000{col 81}{space 4} .2979113{col 94}{space 3} .9637098
{txt}{space 20}dsawviolenceanimals {c |}{col 41}{res}{space 2}-.1922165{col 53}{space 2} .2516211{col 64}{space 1}   -0.76{col 73}{space 3}0.445{col 81}{space 4}-.6853849{col 94}{space 3} .3009518
{txt}{space 31}dinjured {c |}{col 41}{res}{space 2}-.0457318{col 53}{space 2} .1549487{col 64}{space 1}   -0.30{col 73}{space 3}0.768{col 81}{space 4}-.3494256{col 94}{space 3}  .257962
{txt}{space 27}dlostanimals {c |}{col 41}{res}{space 2} .6910619{col 53}{space 2} .1768476{col 64}{space 1}    3.91{col 73}{space 3}0.000{col 81}{space 4} .3444469{col 94}{space 3} 1.037677
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2} -.574127{col 53}{space 2} .1629114{col 64}{space 1}   -3.52{col 73}{space 3}0.000{col 81}{space 4}-.8934275{col 94}{space 3}-.2548264
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2}-1.221924{col 53}{space 2} .7140549{col 64}{space 1}   -1.71{col 73}{space 3}0.087{col 81}{space 4}-2.621446{col 94}{space 3} .1775974
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2}-.8680527{col 53}{space 2} .3031727{col 64}{space 1}   -2.86{col 73}{space 3}0.004{col 81}{space 4} -1.46226{col 94}{space 3} -.273845
{txt}{space 39} {c |}
{space 30}ukrainian {c |}{col 41}{res}{space 2}-.2680926{col 53}{space 2} .3614174{col 64}{space 1}   -0.74{col 73}{space 3}0.458{col 81}{space 4}-.9764577{col 94}{space 3} .4402725
{txt}{space 32}russian {c |}{col 41}{res}{space 2}-.3743527{col 53}{space 2}  .555401{col 64}{space 1}   -0.67{col 73}{space 3}0.500{col 81}{space 4}-1.462919{col 94}{space 3} .7142133
{txt}{space 29}russpeaker {c |}{col 41}{res}{space 2} -.427104{col 53}{space 2} .2419864{col 64}{space 1}   -1.76{col 73}{space 3}0.078{col 81}{space 4}-.9013887{col 94}{space 3} .0471807
{txt}{space 33}female {c |}{col 41}{res}{space 2} .0551442{col 53}{space 2} .1660252{col 64}{space 1}    0.33{col 73}{space 3}0.740{col 81}{space 4}-.2702592{col 94}{space 3} .3805475
{txt}{space 36}age {c |}{col 41}{res}{space 2} -.037505{col 53}{space 2} .0072157{col 64}{space 1}   -5.20{col 73}{space 3}0.000{col 81}{space 4}-.0516476{col 94}{space 3}-.0233624
{txt}{space 30}education {c |}{col 41}{res}{space 2} .1076555{col 53}{space 2} .0584997{col 64}{space 1}    1.84{col 73}{space 3}0.066{col 81}{space 4}-.0070019{col 94}{space 3} .2223128
{txt}{space 33}income {c |}{col 41}{res}{space 2} -.243774{col 53}{space 2}  .102791{col 64}{space 1}   -2.37{col 73}{space 3}0.018{col 81}{space 4}-.4452408{col 94}{space 3}-.0423073
{txt}{space 39} {c |}
{space 29}employment {c |}
{space 4}Servant (without higher education)  {c |}{col 41}{res}{space 2}-.0373722{col 53}{space 2} .3521792{col 64}{space 1}   -0.11{col 73}{space 3}0.915{col 81}{space 4}-.7276308{col 94}{space 3} .6528863
{txt}{space 2}Professional (with higher education)  {c |}{col 41}{res}{space 2}-.2805069{col 53}{space 2}  .286095{col 64}{space 1}   -0.98{col 73}{space 3}0.327{col 81}{space 4}-.8412429{col 94}{space 3} .2802291
{txt}{space 7}Self employed businesswomen/men  {c |}{col 41}{res}{space 2} .0023145{col 53}{space 2} .3821107{col 64}{space 1}    0.01{col 73}{space 3}0.995{col 81}{space 4}-.7466087{col 94}{space 3} .7512377
{txt}{space 18}Entrepreneur, farmer  {c |}{col 41}{res}{space 2}-.3618163{col 53}{space 2} .4050268{col 64}{space 1}   -0.89{col 73}{space 3}0.372{col 81}{space 4}-1.155654{col 94}{space 3} .4320216
{txt}{space 22}Military servant  {c |}{col 41}{res}{space 2} .7708494{col 53}{space 2} .4893115{col 64}{space 1}    1.58{col 73}{space 3}0.115{col 81}{space 4}-.1881835{col 94}{space 3} 1.729882
{txt}{space 27}Householder  {c |}{col 41}{res}{space 2} .3662609{col 53}{space 2} .3413997{col 64}{space 1}    1.07{col 73}{space 3}0.283{col 81}{space 4}-.3028702{col 94}{space 3} 1.035392
{txt}Pension (because of age or disability)  {c |}{col 41}{res}{space 2} .3538583{col 53}{space 2}  .282224{col 64}{space 1}    1.25{col 73}{space 3}0.210{col 81}{space 4}-.1992906{col 94}{space 3} .9070071
{txt}{space 31}Student  {c |}{col 41}{res}{space 2}-.0370238{col 53}{space 2} .6778764{col 64}{space 1}   -0.05{col 73}{space 3}0.956{col 81}{space 4}-1.365637{col 94}{space 3}  1.29159
{txt}{space 28}Unemployed  {c |}{col 41}{res}{space 2} .6052528{col 53}{space 2} .4044309{col 64}{space 1}    1.50{col 73}{space 3}0.135{col 81}{space 4}-.1874172{col 94}{space 3} 1.397923
{txt}{space 39} {c |}
{space 28}urban_rural {c |}{col 41}{res}{space 2} .4520505{col 53}{space 2} .2299533{col 64}{space 1}    1.97{col 73}{space 3}0.049{col 81}{space 4} .0013502{col 94}{space 3} .9027507
{txt}{space 39} {c |}
{space 32}region4 {c |}
{space 34}West  {c |}{col 41}{res}{space 2} -2.35676{col 53}{space 2} .3059132{col 64}{space 1}   -7.70{col 73}{space 3}0.000{col 81}{space 4}-2.956338{col 94}{space 3}-1.757181
{txt}{space 31}Central  {c |}{col 41}{res}{space 2}-2.052972{col 53}{space 2} .2018828{col 64}{space 1}  -10.17{col 73}{space 3}0.000{col 81}{space 4}-2.448655{col 94}{space 3}-1.657289
{txt}{space 33}South  {c |}{col 41}{res}{space 2}-1.115292{col 53}{space 2} .1953857{col 64}{space 1}   -5.71{col 73}{space 3}0.000{col 81}{space 4}-1.498241{col 94}{space 3}-.7323431
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2} 1.124124{col 53}{space 2}   .82048{col 64}{space 1}    1.37{col 73}{space 3}0.171{col 81}{space 4}-.4839869{col 94}{space 3} 2.732236
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Moderating Effects of Animal Ownership
. 
. reg revempmediatorcheck ib3.humanimaltxt##i.ownpets, robust

{txt}Linear regression                               Number of obs     = {res}     1,979
                                                {txt}F(11, 1967)       =  {res}     4.74
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0266
                                                {txt}Root MSE          =    {res} 2.2065

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                    revempmediatorcheck{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      t{col 73}   P>|t|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}humanimaltxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2} .3308755{col 53}{space 2} .2235596{col 64}{space 1}    1.48{col 73}{space 3}0.139{col 81}{space 4} -.107563{col 94}{space 3} .7693141
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2} .2152497{col 53}{space 2} .2111864{col 64}{space 1}    1.02{col 73}{space 3}0.308{col 81}{space 4}-.1989229{col 94}{space 3} .6294222
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}-.6375877{col 53}{space 2} .1836421{col 64}{space 1}   -3.47{col 73}{space 3}0.001{col 81}{space 4}-.9977413{col 94}{space 3}-.2774342
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2}-.1239316{col 53}{space 2} .6570002{col 64}{space 1}   -0.19{col 73}{space 3}0.850{col 81}{space 4}-1.412421{col 94}{space 3} 1.164558
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2} -.535343{col 53}{space 2} .2334183{col 64}{space 1}   -2.29{col 73}{space 3}0.022{col 81}{space 4}-.9931162{col 94}{space 3}-.0775698
{txt}{space 39} {c |}
{space 19}humanimaltxt#ownpets {c |}
{space 22}Treatment 1#Pets  {c |}{col 41}{res}{space 2}-.0734119{col 53}{space 2} .2798311{col 64}{space 1}   -0.26{col 73}{space 3}0.793{col 81}{space 4}-.6222084{col 94}{space 3} .4753846
{txt}{space 14}Treatment 1#Farm Animals  {c |}{col 41}{res}{space 2}-.1591583{col 53}{space 2} 1.054194{col 64}{space 1}   -0.15{col 73}{space 3}0.880{col 81}{space 4}-2.226612{col 94}{space 3} 1.908296
{txt}Treatment 1#Both pets and farm animals  {c |}{col 41}{res}{space 2} .0057496{col 53}{space 2} .3631738{col 64}{space 1}    0.02{col 73}{space 3}0.987{col 81}{space 4}-.7064962{col 94}{space 3} .7179954
{txt}{space 22}Treatment 2#Pets  {c |}{col 41}{res}{space 2}-.3035067{col 53}{space 2} .2698757{col 64}{space 1}   -1.12{col 73}{space 3}0.261{col 81}{space 4}-.8327791{col 94}{space 3} .2257657
{txt}{space 14}Treatment 2#Farm Animals  {c |}{col 41}{res}{space 2}-.6993766{col 53}{space 2} 1.081211{col 64}{space 1}   -0.65{col 73}{space 3}0.518{col 81}{space 4}-2.819815{col 94}{space 3} 1.421062
{txt}Treatment 2#Both pets and farm animals  {c |}{col 41}{res}{space 2} .0009666{col 53}{space 2} .3482797{col 64}{space 1}    0.00{col 73}{space 3}0.998{col 81}{space 4}-.6820695{col 94}{space 3} .6840026
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2} 5.679487{col 53}{space 2} .1427457{col 64}{space 1}   39.79{col 73}{space 3}0.000{col 81}{space 4} 5.399538{col 94}{space 3} 5.959436
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg protectmoral ib3.humanimaltxt##i.ownpets, robust

{txt}Linear regression                               Number of obs     = {res}     1,930
                                                {txt}F(11, 1918)       =  {res}     4.00
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0223
                                                {txt}Root MSE          =    {res}  3.364

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                           protectmoral{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      t{col 73}   P>|t|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}humanimaltxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2}  -.15306{col 53}{space 2} .3166999{col 64}{space 1}   -0.48{col 73}{space 3}0.629{col 81}{space 4}-.7741724{col 94}{space 3} .4680524
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2}-.2059621{col 53}{space 2} .3208915{col 64}{space 1}   -0.64{col 73}{space 3}0.521{col 81}{space 4} -.835295{col 94}{space 3} .4233709
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}-.8233522{col 53}{space 2} .2838422{col 64}{space 1}   -2.90{col 73}{space 3}0.004{col 81}{space 4}-1.380024{col 94}{space 3}-.2666805
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2} 1.087582{col 53}{space 2} .8217579{col 64}{space 1}    1.32{col 73}{space 3}0.186{col 81}{space 4}-.5240512{col 94}{space 3} 2.699215
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2}-.8830065{col 53}{space 2} .3967758{col 64}{space 1}   -2.23{col 73}{space 3}0.026{col 81}{space 4}-1.661164{col 94}{space 3}-.1048493
{txt}{space 39} {c |}
{space 19}humanimaltxt#ownpets {c |}
{space 22}Treatment 1#Pets  {c |}{col 41}{res}{space 2} .0830361{col 53}{space 2} .4129597{col 64}{space 1}    0.20{col 73}{space 3}0.841{col 81}{space 4}-.7268611{col 94}{space 3} .8929333
{txt}{space 14}Treatment 1#Farm Animals  {c |}{col 41}{res}{space 2} .2493167{col 53}{space 2}  1.30824{col 64}{space 1}    0.19{col 73}{space 3}0.849{col 81}{space 4}-2.316405{col 94}{space 3} 2.815039
{txt}Treatment 1#Both pets and farm animals  {c |}{col 41}{res}{space 2} 1.231491{col 53}{space 2} .5514761{col 64}{space 1}    2.23{col 73}{space 3}0.026{col 81}{space 4} .1499356{col 94}{space 3} 2.313047
{txt}{space 22}Treatment 2#Pets  {c |}{col 41}{res}{space 2}-.1612696{col 53}{space 2} .4255384{col 64}{space 1}   -0.38{col 73}{space 3}0.705{col 81}{space 4}-.9958362{col 94}{space 3}  .673297
{txt}{space 14}Treatment 2#Farm Animals  {c |}{col 41}{res}{space 2}-.5089701{col 53}{space 2} 1.305086{col 64}{space 1}   -0.39{col 73}{space 3}0.697{col 81}{space 4}-3.068507{col 94}{space 3} 2.050567
{txt}Treatment 2#Both pets and farm animals  {c |}{col 41}{res}{space 2} 1.074846{col 53}{space 2} .5492525{col 64}{space 1}    1.96{col 73}{space 3}0.051{col 81}{space 4}-.0023489{col 94}{space 3} 2.152041
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2} 6.088889{col 53}{space 2} .2165949{col 64}{space 1}   28.11{col 73}{space 3}0.000{col 81}{space 4} 5.664103{col 94}{space 3} 6.513675
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg feelmorepain ib3.humanimaltxt##i.ownpets, robust

{txt}Linear regression                               Number of obs     = {res}     1,936
                                                {txt}F(11, 1924)       =  {res}     5.86
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0308
                                                {txt}Root MSE          =    {res}  3.465

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                           feelmorepain{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      t{col 73}   P>|t|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}humanimaltxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2}-.3879524{col 53}{space 2}  .337663{col 64}{space 1}   -1.15{col 73}{space 3}0.251{col 81}{space 4}-1.050176{col 94}{space 3} .2742715
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2}-.2784157{col 53}{space 2} .3382485{col 64}{space 1}   -0.82{col 73}{space 3}0.411{col 81}{space 4}-.9417878{col 94}{space 3} .3849564
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}-1.402127{col 53}{space 2} .2909723{col 64}{space 1}   -4.82{col 73}{space 3}0.000{col 81}{space 4}-1.972781{col 94}{space 3}-.8314728
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2} 1.376598{col 53}{space 2} .7931461{col 64}{space 1}    1.74{col 73}{space 3}0.083{col 81}{space 4}-.1789185{col 94}{space 3} 2.932114
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2} -.197816{col 53}{space 2} .3741481{col 64}{space 1}   -0.53{col 73}{space 3}0.597{col 81}{space 4}-.9315944{col 94}{space 3} .5359623
{txt}{space 39} {c |}
{space 19}humanimaltxt#ownpets {c |}
{space 22}Treatment 1#Pets  {c |}{col 41}{res}{space 2} .7297596{col 53}{space 2} .4364019{col 64}{space 1}    1.67{col 73}{space 3}0.095{col 81}{space 4}-.1261108{col 94}{space 3}  1.58563
{txt}{space 14}Treatment 1#Farm Animals  {c |}{col 41}{res}{space 2} .8677504{col 53}{space 2} 1.296681{col 64}{space 1}    0.67{col 73}{space 3}0.503{col 81}{space 4}-1.675298{col 94}{space 3} 3.410799
{txt}Treatment 1#Both pets and farm animals  {c |}{col 41}{res}{space 2} .3212552{col 53}{space 2} .5596526{col 64}{space 1}    0.57{col 73}{space 3}0.566{col 81}{space 4}-.7763342{col 94}{space 3} 1.418845
{txt}{space 22}Treatment 2#Pets  {c |}{col 41}{res}{space 2} .6315964{col 53}{space 2} .4382095{col 64}{space 1}    1.44{col 73}{space 3}0.150{col 81}{space 4}-.2278191{col 94}{space 3} 1.491012
{txt}{space 14}Treatment 2#Farm Animals  {c |}{col 41}{res}{space 2}-.6660287{col 53}{space 2} 1.135437{col 64}{space 1}   -0.59{col 73}{space 3}0.558{col 81}{space 4}-2.892846{col 94}{space 3} 1.560788
{txt}Treatment 2#Both pets and farm animals  {c |}{col 41}{res}{space 2} .7962639{col 53}{space 2} .5453483{col 64}{space 1}    1.46{col 73}{space 3}0.144{col 81}{space 4} -.273272{col 94}{space 3}   1.8658
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2} 6.234513{col 53}{space 2} .2248632{col 64}{space 1}   27.73{col 73}{space 3}0.000{col 81}{space 4} 5.793512{col 94}{space 3} 6.675514
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg protectmore ib3.humanimaltxt##i.ownpets, robust

{txt}Linear regression                               Number of obs     = {res}     1,957
                                                {txt}F(11, 1945)       =  {res}     7.56
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0283
                                                {txt}Root MSE          =    {res} 3.0643

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                            protectmore{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      t{col 73}   P>|t|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}humanimaltxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2}-.0764302{col 53}{space 2} .2954738{col 64}{space 1}   -0.26{col 73}{space 3}0.796{col 81}{space 4}-.6559088{col 94}{space 3} .5030484
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2} .1618473{col 53}{space 2} .2844381{col 64}{space 1}    0.57{col 73}{space 3}0.569{col 81}{space 4}-.3959883{col 94}{space 3} .7196829
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}-1.092606{col 53}{space 2} .2555033{col 64}{space 1}   -4.28{col 73}{space 3}0.000{col 81}{space 4}-1.593695{col 94}{space 3}-.5915172
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2} 2.024155{col 53}{space 2}  .450213{col 64}{space 1}    4.50{col 73}{space 3}0.000{col 81}{space 4} 1.141204{col 94}{space 3} 2.907105
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2}-.5778656{col 53}{space 2} .3463571{col 64}{space 1}   -1.67{col 73}{space 3}0.095{col 81}{space 4}-1.257136{col 94}{space 3} .1014045
{txt}{space 39} {c |}
{space 19}humanimaltxt#ownpets {c |}
{space 22}Treatment 1#Pets  {c |}{col 41}{res}{space 2} .3937682{col 53}{space 2} .3868231{col 64}{space 1}    1.02{col 73}{space 3}0.309{col 81}{space 4}-.3648632{col 94}{space 3}   1.1524
{txt}{space 14}Treatment 1#Farm Animals  {c |}{col 41}{res}{space 2}-.6710445{col 53}{space 2} .8008471{col 64}{space 1}   -0.84{col 73}{space 3}0.402{col 81}{space 4}-2.241653{col 94}{space 3} .8995643
{txt}Treatment 1#Both pets and farm animals  {c |}{col 41}{res}{space 2} .5673393{col 53}{space 2} .5066587{col 64}{space 1}    1.12{col 73}{space 3}0.263{col 81}{space 4}-.4263119{col 94}{space 3}  1.56099
{txt}{space 22}Treatment 2#Pets  {c |}{col 41}{res}{space 2} .4538024{col 53}{space 2} .3766473{col 64}{space 1}    1.20{col 73}{space 3}0.228{col 81}{space 4}-.2848723{col 94}{space 3} 1.192477
{txt}{space 14}Treatment 2#Farm Animals  {c |}{col 41}{res}{space 2}-1.058673{col 53}{space 2} .8126652{col 64}{space 1}   -1.30{col 73}{space 3}0.193{col 81}{space 4}-2.652459{col 94}{space 3} .5351137
{txt}Treatment 2#Both pets and farm animals  {c |}{col 41}{res}{space 2} .3379113{col 53}{space 2} .4903393{col 64}{space 1}    0.69{col 73}{space 3}0.491{col 81}{space 4}-.6237344{col 94}{space 3} 1.299557
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2} 7.086957{col 53}{space 2} .1889098{col 64}{space 1}   37.52{col 73}{space 3}0.000{col 81}{space 4}  6.71647{col 94}{space 3} 7.457443
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg revaidmediatorcheck ib3.humanimalaidtxt##i.ownpets, robust

{txt}Linear regression                               Number of obs     = {res}     1,969
                                                {txt}F(11, 1957)       =  {res}     4.95
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0275
                                                {txt}Root MSE          =    {res} 2.1101

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                    revaidmediatorcheck{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      t{col 73}   P>|t|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}humanimalaidtxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2} .1757881{col 53}{space 2} .2233862{col 64}{space 1}    0.79{col 73}{space 3}0.431{col 81}{space 4}-.2623119{col 94}{space 3}  .613888
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2} .1419131{col 53}{space 2} .2307306{col 64}{space 1}    0.62{col 73}{space 3}0.539{col 81}{space 4}-.3105905{col 94}{space 3} .5944167
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}-.4117044{col 53}{space 2} .2003324{col 64}{space 1}   -2.06{col 73}{space 3}0.040{col 81}{space 4}-.8045917{col 94}{space 3}-.0188171
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2} .7615023{col 53}{space 2} .6242361{col 64}{space 1}    1.22{col 73}{space 3}0.223{col 81}{space 4}-.4627351{col 94}{space 3}  1.98574
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2}-.5181273{col 53}{space 2} .2683144{col 64}{space 1}   -1.93{col 73}{space 3}0.054{col 81}{space 4}-1.044339{col 94}{space 3} .0080847
{txt}{space 39} {c |}
{space 16}humanimalaidtxt#ownpets {c |}
{space 22}Treatment 1#Pets  {c |}{col 41}{res}{space 2}-.3147025{col 53}{space 2} .2770967{col 64}{space 1}   -1.14{col 73}{space 3}0.256{col 81}{space 4}-.8581381{col 94}{space 3} .2287331
{txt}{space 14}Treatment 1#Farm Animals  {c |}{col 41}{res}{space 2}-.9091214{col 53}{space 2} .8970719{col 64}{space 1}   -1.01{col 73}{space 3}0.311{col 81}{space 4}-2.668438{col 94}{space 3} .8501953
{txt}Treatment 1#Both pets and farm animals  {c |}{col 41}{res}{space 2}-.2135626{col 53}{space 2} .3506113{col 64}{space 1}   -0.61{col 73}{space 3}0.543{col 81}{space 4}-.9011734{col 94}{space 3} .4740483
{txt}{space 22}Treatment 2#Pets  {c |}{col 41}{res}{space 2}-.5296259{col 53}{space 2} .2764777{col 64}{space 1}   -1.92{col 73}{space 3}0.056{col 81}{space 4}-1.071848{col 94}{space 3} .0125958
{txt}{space 14}Treatment 2#Farm Animals  {c |}{col 41}{res}{space 2}-.9377464{col 53}{space 2} .9352722{col 64}{space 1}   -1.00{col 73}{space 3}0.316{col 81}{space 4}-2.771981{col 94}{space 3} .8964878
{txt}Treatment 2#Both pets and farm animals  {c |}{col 41}{res}{space 2}-.1289501{col 53}{space 2} .3569496{col 64}{space 1}   -0.36{col 73}{space 3}0.718{col 81}{space 4}-.8289914{col 94}{space 3} .5710911
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2} 5.971831{col 53}{space 2} .1666175{col 64}{space 1}   35.84{col 73}{space 3}0.000{col 81}{space 4} 5.645065{col 94}{space 3} 6.298597
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg providemoral ib3.humanimalaidtxt##i.ownpets, robust

{txt}Linear regression                               Number of obs     = {res}     1,943
                                                {txt}F(11, 1931)       =  {res}     3.29
                                                {txt}Prob > F          = {res}    0.0002
                                                {txt}R-squared         = {res}    0.0184
                                                {txt}Root MSE          =    {res} 3.1014

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                           providemoral{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      t{col 73}   P>|t|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}humanimalaidtxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2}-.1835458{col 53}{space 2} .2946905{col 64}{space 1}   -0.62{col 73}{space 3}0.533{col 81}{space 4}-.7614908{col 94}{space 3} .3943993
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2}-.1604094{col 53}{space 2} .3021186{col 64}{space 1}   -0.53{col 73}{space 3}0.596{col 81}{space 4}-.7529223{col 94}{space 3} .4321035
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}-.8726152{col 53}{space 2} .2730322{col 64}{space 1}   -3.20{col 73}{space 3}0.001{col 81}{space 4}-1.408084{col 94}{space 3}-.3371463
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2} .7829404{col 53}{space 2}  .851719{col 64}{space 1}    0.92{col 73}{space 3}0.358{col 81}{space 4}-.8874452{col 94}{space 3} 2.453326
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2}-.6596498{col 53}{space 2} .3564931{col 64}{space 1}   -1.85{col 73}{space 3}0.064{col 81}{space 4}-1.358802{col 94}{space 3} .0395021
{txt}{space 39} {c |}
{space 16}humanimalaidtxt#ownpets {c |}
{space 22}Treatment 1#Pets  {c |}{col 41}{res}{space 2} .0123817{col 53}{space 2} .3869686{col 64}{space 1}    0.03{col 73}{space 3}0.974{col 81}{space 4}-.7465386{col 94}{space 3}  .771302
{txt}{space 14}Treatment 1#Farm Animals  {c |}{col 41}{res}{space 2} .2549743{col 53}{space 2} 1.297692{col 64}{space 1}    0.20{col 73}{space 3}0.844{col 81}{space 4} -2.29005{col 94}{space 3} 2.799999
{txt}Treatment 1#Both pets and farm animals  {c |}{col 41}{res}{space 2} .3225645{col 53}{space 2} .5019594{col 64}{space 1}    0.64{col 73}{space 3}0.521{col 81}{space 4}-.6618749{col 94}{space 3} 1.307004
{txt}{space 22}Treatment 2#Pets  {c |}{col 41}{res}{space 2} .0244652{col 53}{space 2} .3917665{col 64}{space 1}    0.06{col 73}{space 3}0.950{col 81}{space 4}-.7438646{col 94}{space 3} .7927949
{txt}{space 14}Treatment 2#Farm Animals  {c |}{col 41}{res}{space 2} -.568162{col 53}{space 2} 1.239249{col 64}{space 1}   -0.46{col 73}{space 3}0.647{col 81}{space 4}-2.998568{col 94}{space 3} 1.862244
{txt}Treatment 2#Both pets and farm animals  {c |}{col 41}{res}{space 2} .2417358{col 53}{space 2} .5127864{col 64}{space 1}    0.47{col 73}{space 3}0.637{col 81}{space 4}-.7639376{col 94}{space 3} 1.247409
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2} 6.145631{col 53}{space 2} .2073731{col 64}{space 1}   29.64{col 73}{space 3}0.000{col 81}{space 4} 5.738932{col 94}{space 3}  6.55233
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg selfhelp ib3.humanimalaidtxt##i.ownpets, robust

{txt}Linear regression                               Number of obs     = {res}     1,963
                                                {txt}F(11, 1951)       =  {res}     0.83
                                                {txt}Prob > F          = {res}    0.6110
                                                {txt}R-squared         = {res}    0.0048
                                                {txt}Root MSE          =    {res} 2.6681

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                               selfhelp{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      t{col 73}   P>|t|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}humanimalaidtxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2} .2295388{col 53}{space 2} .2607231{col 64}{space 1}    0.88{col 73}{space 3}0.379{col 81}{space 4}-.2817863{col 94}{space 3}  .740864
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2} .0089651{col 53}{space 2} .2699498{col 64}{space 1}    0.03{col 73}{space 3}0.974{col 81}{space 4}-.5204553{col 94}{space 3} .5383854
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2} .4317308{col 53}{space 2} .2373051{col 64}{space 1}    1.82{col 73}{space 3}0.069{col 81}{space 4}-.0336674{col 94}{space 3}  .897129
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2} .2775641{col 53}{space 2} .6328583{col 64}{space 1}    0.44{col 73}{space 3}0.661{col 81}{space 4}-.9635853{col 94}{space 3} 1.518714
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2} .2543225{col 53}{space 2}  .310188{col 64}{space 1}    0.82{col 73}{space 3}0.412{col 81}{space 4}-.3540122{col 94}{space 3} .8626572
{txt}{space 39} {c |}
{space 16}humanimalaidtxt#ownpets {c |}
{space 22}Treatment 1#Pets  {c |}{col 41}{res}{space 2}-.3754726{col 53}{space 2} .3355185{col 64}{space 1}   -1.12{col 73}{space 3}0.263{col 81}{space 4}-1.033485{col 94}{space 3} .2825398
{txt}{space 14}Treatment 1#Farm Animals  {c |}{col 41}{res}{space 2} .4832817{col 53}{space 2}  1.03664{col 64}{space 1}    0.47{col 73}{space 3}0.641{col 81}{space 4}-1.549756{col 94}{space 3}  2.51632
{txt}Treatment 1#Both pets and farm animals  {c |}{col 41}{res}{space 2}-.4646306{col 53}{space 2} .4442695{col 64}{space 1}   -1.05{col 73}{space 3}0.296{col 81}{space 4}-1.335923{col 94}{space 3} .4066623
{txt}{space 22}Treatment 2#Pets  {c |}{col 41}{res}{space 2} .0347652{col 53}{space 2} .3399111{col 64}{space 1}    0.10{col 73}{space 3}0.919{col 81}{space 4}-.6318619{col 94}{space 3} .7013923
{txt}{space 14}Treatment 2#Farm Animals  {c |}{col 41}{res}{space 2} .1077016{col 53}{space 2} .9843545{col 64}{space 1}    0.11{col 73}{space 3}0.913{col 81}{space 4}-1.822795{col 94}{space 3} 2.038199
{txt}Treatment 2#Both pets and farm animals  {c |}{col 41}{res}{space 2} .1139529{col 53}{space 2} .4326918{col 64}{space 1}    0.26{col 73}{space 3}0.792{col 81}{space 4}-.7346339{col 94}{space 3} .9625396
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2} 7.855769{col 53}{space 2} .1871257{col 64}{space 1}   41.98{col 73}{space 3}0.000{col 81}{space 4} 7.488782{col 94}{space 3} 8.222757
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg giveidp100rnd ib3.humanimalaidtxt##i.ownpets, robust

{txt}Linear regression                               Number of obs     = {res}     1,979
                                                {txt}F(11, 1967)       =  {res}     5.90
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0321
                                                {txt}Root MSE          =    {res} 2.2277

{txt}{hline 40}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 41}{c |}{col 53}    Robust
{col 1}                          giveidp100rnd{col 41}{c |} Coefficient{col 53}  std. err.{col 65}      t{col 73}   P>|t|{col 81}     [95% con{col 94}f. interval]
{hline 40}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}humanimalaidtxt {c |}
{space 27}Treatment 1  {c |}{col 41}{res}{space 2} .2233996{col 53}{space 2} .2179497{col 64}{space 1}    1.03{col 73}{space 3}0.305{col 81}{space 4}-.2040371{col 94}{space 3} .6508363
{txt}{space 27}Treatment 2  {c |}{col 41}{res}{space 2}-.1514198{col 53}{space 2} .2194869{col 64}{space 1}   -0.69{col 73}{space 3}0.490{col 81}{space 4} -.581871{col 94}{space 3} .2790314
{txt}{space 39} {c |}
{space 32}ownpets {c |}
{space 34}Pets  {c |}{col 41}{res}{space 2}-.5247371{col 53}{space 2} .2020067{col 64}{space 1}   -2.60{col 73}{space 3}0.009{col 81}{space 4}-.9209067{col 94}{space 3}-.1285676
{txt}{space 26}Farm Animals  {c |}{col 41}{res}{space 2} .3444593{col 53}{space 2} .5456211{col 64}{space 1}    0.63{col 73}{space 3}0.528{col 81}{space 4}-.7255969{col 94}{space 3} 1.414515
{txt}{space 12}Both pets and farm animals  {c |}{col 41}{res}{space 2}-.6841121{col 53}{space 2} .2488437{col 64}{space 1}   -2.75{col 73}{space 3}0.006{col 81}{space 4}-1.172137{col 94}{space 3}-.1960872
{txt}{space 39} {c |}
{space 16}humanimalaidtxt#ownpets {c |}
{space 22}Treatment 1#Pets  {c |}{col 41}{res}{space 2}-.4179097{col 53}{space 2} .2784104{col 64}{space 1}   -1.50{col 73}{space 3}0.134{col 81}{space 4}  -.96392{col 94}{space 3} .1281006
{txt}{space 14}Treatment 1#Farm Animals  {c |}{col 41}{res}{space 2} -.401971{col 53}{space 2} .8014569{col 64}{space 1}   -0.50{col 73}{space 3}0.616{col 81}{space 4}-1.973765{col 94}{space 3} 1.169823
{txt}Treatment 1#Both pets and farm animals  {c |}{col 41}{res}{space 2}-.3376853{col 53}{space 2}  .353368{col 64}{space 1}   -0.96{col 73}{space 3}0.339{col 81}{space 4}  -1.0307{col 94}{space 3} .3553298
{txt}{space 22}Treatment 2#Pets  {c |}{col 41}{res}{space 2}-.3427378{col 53}{space 2} .2842475{col 64}{space 1}   -1.21{col 73}{space 3}0.228{col 81}{space 4}-.9001956{col 94}{space 3} .2147201
{txt}{space 14}Treatment 2#Farm Animals  {c |}{col 41}{res}{space 2}-.4021516{col 53}{space 2} .7655904{col 64}{space 1}   -0.53{col 73}{space 3}0.599{col 81}{space 4}-1.903605{col 94}{space 3} 1.099302
{txt}Treatment 2#Both pets and farm animals  {c |}{col 41}{res}{space 2} .2133246{col 53}{space 2} .3626649{col 64}{space 1}    0.59{col 73}{space 3}0.556{col 81}{space 4}-.4979232{col 94}{space 3} .9245724
{txt}{space 39} {c |}
{space 34}_cons {c |}{col 41}{res}{space 2} 6.584112{col 53}{space 2} .1612799{col 64}{space 1}   40.82{col 73}{space 3}0.000{col 81}{space 4} 6.267815{col 94}{space 3}  6.90041
{txt}{hline 40}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg punishment humandeathtxt##i.ownpets, robust

{txt}Linear regression                               Number of obs     = {res}     1,744
                                                {txt}F(7, 1736)        =  {res}     6.35
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0258
                                                {txt}Root MSE          =    {res} 1.3741

{txt}{hline 30}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 31}{c |}{col 43}    Robust
{col 1}                   punishment{col 31}{c |} Coefficient{col 43}  std. err.{col 55}      t{col 63}   P>|t|{col 71}     [95% con{col 84}f. interval]
{hline 30}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}1.humandeathtxt {c |}{col 31}{res}{space 2} .4833748{col 43}{space 2} .1148189{col 54}{space 1}    4.21{col 63}{space 3}0.000{col 71}{space 4} .2581769{col 84}{space 3} .7085726
{txt}{space 29} {c |}
{space 22}ownpets {c |}
{space 24}Pets  {c |}{col 31}{res}{space 2} .1927273{col 43}{space 2} .1069153{col 54}{space 1}    1.80{col 63}{space 3}0.072{col 71}{space 4}-.0169691{col 84}{space 3} .4024236
{txt}{space 16}Farm Animals  {c |}{col 31}{res}{space 2} .6109091{col 43}{space 2} .2905071{col 54}{space 1}    2.10{col 63}{space 3}0.036{col 71}{space 4} .0411283{col 84}{space 3}  1.18069
{txt}{space 2}Both pets and farm animals  {c |}{col 31}{res}{space 2} .1353846{col 43}{space 2} .1492299{col 54}{space 1}    0.91{col 63}{space 3}0.364{col 71}{space 4}-.1573047{col 84}{space 3}  .428074
{txt}{space 29} {c |}
{space 8}humandeathtxt#ownpets {c |}
{space 22}1#Pets  {c |}{col 31}{res}{space 2}-.1567233{col 43}{space 2} .1473038{col 54}{space 1}   -1.06{col 63}{space 3}0.288{col 71}{space 4}-.4456349{col 84}{space 3} .1321883
{txt}{space 14}1#Farm Animals  {c |}{col 31}{res}{space 2} -.246001{col 43}{space 2} .4859074{col 54}{space 1}   -0.51{col 63}{space 3}0.613{col 71}{space 4}-1.199026{col 84}{space 3} .7070244
{txt}1#Both pets and farm animals  {c |}{col 31}{res}{space 2} .0263169{col 43}{space 2} .2069949{col 54}{space 1}    0.13{col 63}{space 3}0.899{col 71}{space 4}-.3796688{col 84}{space 3} .4323026
{txt}{space 29} {c |}
{space 24}_cons {c |}{col 31}{res}{space 2} 3.207273{col 43}{space 2} .0838435{col 54}{space 1}   38.25{col 63}{space 3}0.000{col 71}{space 4} 3.042828{col 84}{space 3} 3.371718
{txt}{hline 30}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Survey Sampling Methodology (Moldova)
. 
. *Note: Use "PoP Moldova replication data.dta"
. 
. *Concern for Human over Animal Suffering (OLS Regression)
. 
. *histogram revanimal1, discrete percent addlabels addlabopts(mlabformat(%2.1f))
. 
. *Concern for Human over Animal Suffering (Figure)
. 
. *reg revanimal1 gender age ib3.education i.location ib10.employment i.language ib4.income, robust
. 
. *Survey Sampling Methodology (United States)
. 
. *Note: Use "PoP US replication data.dta"
. 
. *reg revanimal1_1 ib2.gender_1 ageyear_1 i.education_1 hhincome_1 ib3.Region_1 ib3.partyid_1 ib2.race_1 ib2.latino_1, robust
. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\swhitt\Desktop\PoP replication log file.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 1 May 2025, 07:29:12
{txt}{.-}
{smcl}
{txt}{sf}{ul off}